# Claude Code transcript export — session 38516fbd-...
# exported 2026-04-22T12:57:37.021635
# source: /home/adamginsburg/.claude/projects/-orange-adamginsburg-jwst-sickle/38516fbd-e4af-48f6-a0ee-572ff217e010.jsonl
# tool_use inputs trimmed at 2k; tool_result blocks trimmed at 3k; internal reasoning omitted
================================================================================
--- [] permission-mode ---
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--- [] file-history-snapshot ---
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--- [2026-04-21T01:16:19.984Z] user ---
Caveat: The messages below were generated by the user while running local commands. DO NOT respond to these messages or otherwise consider them in your response unless the user explicitly asks you to.
--- [2026-04-21T01:16:19.984Z] user ---
/model
model
--- [2026-04-21T01:16:19.984Z] user ---
Set model to [1mHaiku 4.5[22m
--- [] file-history-snapshot ---
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--- [2026-04-21T01:17:54.723Z] user ---
Help me make an agents.md file for this folder. Base it on /home/adamginsburg/.copilot_instructions
--- [2026-04-21T01:17:54.723Z] attachment ---
{"parentUuid": "59c87485-966c-41cc-860e-7d9c982c9390", "isSidechain": false, "attachment": {"type": "skill_listing", "content": "- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, s
--- [2026-04-21T01:17:56.780Z] assistant ---
--- [2026-04-21T01:17:56.851Z] assistant ---
[TOOL USE: Read] {"file_path": "/home/adamginsburg/.copilot_instructions"}
--- [2026-04-21T01:18:01.857Z] user ---
[TOOL RESULT]
1 Do not use try/except blocks when writing python code. If an exception is encountered, it should be exposed to the user and corrected. There are no circumstances ever in which a bare 'except:' should be used. There are a very limited set of circumstances in which try/except ExceptionType: can be used, but you must ask permission before using that construct.
2
3
4 Use conda environments, managed by mamba, for python code. Default to /blue/adamginsburg/adamginsburg/miniconda3/envs/python313, but you can use others if needed. Do not install or upgrade packages in existing environments.
5
6
7 Use slurm for big jobs. The default qos is astronomy-dept-b and user is astronomy-dept.
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
--- [2026-04-21T01:18:04.211Z] assistant ---
--- [2026-04-21T01:18:04.656Z] assistant ---
Now I'll create an agents.md file for this project based on those instructions.
--- [2026-04-21T01:18:07.677Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/agents.md", "content": "# Agents Guidelines for JWST Sickle\n\n## Error Handling\n\n**Do not use try/except blocks** when writing Python code. If an exception is encountered, it should be exposed to the user and corrected.\n\n- **Never use bare `except:` clauses** under any circumstances\n- `try/except ExceptionType:` blocks should only be used in very limited circumstances and require explicit permission before implementation\n- Let exceptions propagate to the user so they can be properly diagnosed and fixed\n\n## Python Environments\n\nUse **conda environments, managed by mamba**, for Python code.\n\n- **Default environment**: `/blue/adamginsburg/adamginsburg/miniconda3/envs/python313`\n- Other environments may be used if needed\n- **Do not install or upgrade packages** in existing environments\n- Create new environments as needed for isolated dependencies\n\n## Job Submission\n\nUse **SLURM** for big computational jobs.\n\n- **Default QOS**: `astronomy-dept-b`\n- **Default user**: `astronomy-dept`\n- Submit large jobs to the cluster rather than running locally\n"}
--- [] file-history-snapshot ---
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--- [2026-04-21T01:18:14.458Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/agents.md
--- [2026-04-21T01:18:16.464Z] assistant ---
--- [2026-04-21T01:18:17.175Z] assistant ---
Created `agents.md` with guidelines for:
- **Error handling**: No try/except blocks unless absolutely necessary and with permission
- **Python environments**: Use mamba-managed conda, default to `/blue/adamginsburg/adamginsburg/miniconda3/envs/python313`
- **Job submission**: Use SLURM with `astronomy-dept-b` QOS and `astronomy-dept` user for large jobs
The file is ready for future agents working on this project.
--- [2026-04-21T01:18:30.723Z] user ---
pwd
--- [] file-history-snapshot ---
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--- [2026-04-21T01:18:32.132Z] assistant ---
--- [2026-04-21T01:18:32.195Z] assistant ---
[TOOL USE: Bash] {"command": "pwd", "description": "Show current working directory"}
--- [2026-04-21T01:18:52.125Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
/orange/adamginsburg/jwst/sickle
--- [2026-04-21T01:18:54.063Z] assistant ---
--- [2026-04-21T01:18:54.071Z] assistant ---
You're in `/orange/adamginsburg/jwst/sickle`
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--- [2026-04-21T01:20:29.498Z] user ---
Help me figure out why my PSF photometry is overfitting some sources. The overfitting experiment is this file: /orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/overfitting_experiment_f480m.py and the description is here: /orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/oversubtraction_fixing.md. The actual production code is /orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/crowdsource_catalogs_long.py.
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "Help me figure out why my PSF photometry is overfitting some sources. The overfitting experiment is this file: /orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/overfitting_experiment_f48\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:20:31.468Z] assistant ---
--- [2026-04-21T01:20:31.890Z] assistant ---
[TOOL USE: Read] {"file_path": "/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/oversubtraction_fixing.md"}
--- [2026-04-21T01:20:32.068Z] assistant ---
[TOOL USE: Read] {"file_path": "/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/overfitting_experiment_f480m.py"}
--- [2026-04-21T01:20:32.309Z] assistant ---
[TOOL USE: Read] {"file_path": "/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/crowdsource_catalogs_long.py"}
--- [2026-04-21T01:20:35.195Z] user ---
[TOOL RESULT]
1 # Oversubtraction Problem Fixing (human-written section)
2
3 The residual maps show that many stars are oversubtracted (their peaks are overfitted).
4
5 This straight up shouldn't be happening - this is not an expected effect of PSF photometry, somehow the fits are systematically too high. This is really, really bad because it means that the photometry is systematically overestimatd, which is a total disaster.
6
7 Develop a strategy to fight this overfitting problem.
8
9 Given the statement of this problem, it is absurdly critical - like, so important and obvious that it should go unstated - that we have to fit STPSFs, NOT GAUSSIANS.
10
11 Also obviously, we need to perform the fitting test on a single frame (e.g., a crf or destreak file), just like we do for the cataloging script. The overfitting experiment now is incorrectly being performed on i2d data, which do not have well-defined PSFs.
12
13 To find the overfit stars, perform starfinding on the residual image multiplied by -1. Use that list for further diagnostics. The mosaiced residual is a great place to ID these stars, even though we won't and can't use mosaiced images for fitting.
14
15 Let's then operate on a cutout of a few (say, 5-10) selected stars and experiment with a range of fitting parameters (using photutils tools). See which parameters prevent the oversubtraction. Maybe iterative photometry does better? Maybe the key is doing something different with local background estimation?
16
17 Write the plan into this file (oversubtraction_fixing.md) as new sections - do not delete this human-written section.
18
19 Perform the experiments on sickle F480M data and store the outputs in
20 /orange/adamginsburg/jwst/sickle/overfitting_experiments
21 as needed.
22
23 A sample of hand-selected overfitted stars is in the region file:
24 /orange/adamginsburg/jwst/sickle/regions_/diagnostic_oversubtracted_stars_bigger.reg
25 the oversubtracted star finding should recover most or all of these.
26
27
28 We had already done some work in this regard:
29 /orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/diagnose_oversubtracted_regions.py
30
31 As a general rule, DO NOT MAKE VIRTUALENVS. The pipeline is meant to, and must, run in this environment:
32 /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python
33
34
35 ## New Strategy: detect oversubtracted stars from negative residuals, then fit controlled cutouts
36
37 ### Goal
38 Build a reproducible workflow that:
39 1. identifies likely oversubtracted stars directly from the residual image,
40 2. checks recovery against hand-selected problematic stars, and
41 3. runs controlled 5-10 star photutils fitting experiments to isolate which fit settings reduce central negative residuals.
42
43 ### Data/products used (Sickle F480M)
44 - Science image:
45 - `/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits`
46 - Residual image tested:
47 - `/orange/ada
... (+6006 chars truncated)
--- [2026-04-21T01:20:35.419Z] user ---
[TOOL RESULT]
1 #!/usr/bin/env python
2
3 from __future__ import annotations
4
5 import argparse
6 import glob
7 import re
8 from pathlib import Path
9
10 import numpy as np
11 import regions
12 from astropy import units as u
13 from astropy.coordinates import SkyCoord
14 from astropy.convolution import Gaussian2DKernel, convolve_fft, interpolate_replace_nans
15 from astropy.io import fits
16 from astropy.modeling.fitting import LevMarLSQFitter
17 from astropy.stats import mad_std, sigma_clipped_stats
18 from astropy.table import Table
19 from astropy.visualization import simple_norm
20 from astropy.wcs import WCS
21 from matplotlib.backends.backend_pdf import PdfPages
22 from photutils.background import LocalBackground
23 from photutils.detection import DAOStarFinder
24 from photutils.psf import IterativePSFPhotometry, PSFPhotometry
25 from stpsf.utils import to_griddedpsfmodel
26
27 import matplotlib
28 matplotlib.use('Agg')
29 import matplotlib.pyplot as plt
30
31 from astropy.wcs import FITSFixedWarning
32 import warnings
33 warnings.simplefilter('ignore', category=FITSFixedWarning)
34
35
36 SATURATED_DQ_BIT = 2
37
38
39 def load_fits_data_and_wcs(filename: Path):
40 with fits.open(filename) as hdul:
41 if 'SCI' in hdul:
42 data = np.asarray(hdul['SCI'].data, dtype=float)
43 wcs = WCS(hdul['SCI'].header)
44 else:
45 data = np.asarray(hdul[1].data, dtype=float)
46 wcs = WCS(hdul[1].header)
47 return data, wcs
48
49
50 def load_fits_bundle(filename: Path):
51 with fits.open(filename) as hdul:
52 if 'SCI' in hdul:
53 data = np.asarray(hdul['SCI'].data, dtype=float)
54 wcs = WCS(hdul['SCI'].header)
55 else:
56 data = np.asarray(hdul[1].data, dtype=float)
57 wcs = WCS(hdul[1].header)
58
59 if 'ERR' in hdul:
60 err = np.asarray(hdul['ERR'].data, dtype=float)
61 elif len(hdul) > 2 and hdul[2].data is not None:
62 err = np.asarray(hdul[2].data, dtype=float)
63 else:
64 err = None
65
66 if 'DQ' in hdul:
67 dq = np.asarray(hdul['DQ'].data)
68 elif len(hdul) > 3 and hdul[3].data is not None:
69 dq = np.asarray(hdul[3].data)
70 else:
71 dq = None
72
73 if 'WHT' in hdul:
74 wht = np.asarray(hdul['WHT'].data, dtype=float)
75 else:
76 wht = None
77
78 return data, wcs, err, dq, wht
79
80
81 def read_point_regions(region_file: Path):
82 regs = regions.Regions.read(region_file)
83 points = [
84 reg for reg in regs
85 if hasattr(reg, 'center') and reg.__class__.__name__.endswith('PointSkyRegion')
86 ]
87 return points
88
89
90 def detect_negative_residual_stars(residual_data, fwhm_pix, sigma_threshold, roundlo, roundhi, sharplo, sharphi):
91 inv_residual = -residual_data
92 finite = np.isfinite(inv_residual)
93 if
... (+65109 chars truncated)
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "Help me figure out why my PSF photometry is overfitting some sources. The overfitting experiment is this file: /orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/overfitting_experiment_f48\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:20:35.577Z] user ---
[TOOL RESULT]
File content (31613 tokens) exceeds maximum allowed tokens (25000). Use offset and limit parameters to read specific portions of the file, or search for specific content instead of reading the whole file.
--- [2026-04-21T01:20:42.104Z] assistant ---
--- [2026-04-21T01:20:43.597Z] assistant ---
I've got the context. The oversubtraction problem is **systematic PSF fitting that's too aggressive**, resulting in overfitted peaks with negative residuals at star centers. The experiment script found that even the best configuration tested (`basic_local2_5_fit5`) still shows ~-50.6 median central residual, indicating the problem isn't solved by parameter tuning alone.
Let me search the production code for the key fitting logic to understand the current approach:
--- [2026-04-21T01:20:44.889Z] assistant ---
[TOOL USE: Grep] {"pattern": "PSFPhotometry|IterativePSFPhotometry|fit_shape|localbkg|LocalBackground", "path": "/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/crowdsource_catalogs_long.py", "output_mode": "content", "head_limit": 50}
--- [2026-04-21T01:20:45.072Z] user ---
[TOOL RESULT]
42:# PSFPhotometry, IterativePSFPhotometry, SourceGrouper present since photutils 1.9
43:from photutils.psf import PSFPhotometry, IterativePSFPhotometry, SourceGrouper
44:# LocalBackground present since photutils 1.9
45:from photutils.background import MMMBackground, MADStdBackgroundRMS, MedianBackground, Background2D, LocalBackground
1930: phot_basic = PSFPhotometry(finder=basic_finder,
1933: localbkg_estimator=LocalBackground(6, 10),
1937: fit_shape=(5, 5),
1970: modsky = phot_basic.make_model_image(data.shape, psf_shape=(21, 21), include_localbkg=False)
2038: phot_iter = IterativePSFPhotometry(finder=daofind_tuned,
2039: localbkg_estimator=LocalBackground(6, 10),
2044: fit_shape=(5, 5),
2079: modsky = phot_iter.make_model_image(data.shape, psf_shape=(21, 21), include_localbkg=False)
--- [2026-04-21T01:20:45.396Z] assistant ---
[TOOL USE: Grep] {"pattern": "def.*photometry|def.*fit|DAOStarFinder", "path": "/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/crowdsource_catalogs_long.py", "output_mode": "content", "head_limit": 30}
--- [2026-04-21T01:20:45.456Z] user ---
[TOOL RESULT]
35:from photutils.detection import DAOStarFinder, IRAFStarFinder
1279: help='DAOStarFinder roundness lower bound')
1283: help='DAOStarFinder roundness upper bound')
1288: help='After --each-exposure, resample all per-exposure residuals into a residual_i2d product by default; this parameter skips that step. Residual kinds are auto-determined based on enabled photometry types.')
1495:def do_photometry_step(options, filtername, module, detector, field, basepath,
1627: daofind_tuned = DAOStarFinder(threshold=daofind_threshold,
1647: daofind_tuned = DAOStarFinder(threshold=daofind_threshold,
1692: extra_finder = DAOStarFinder(threshold=extra_noise_floor,
--- [2026-04-21T01:20:45.469Z] attachment ---
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--- [2026-04-21T01:20:48.614Z] assistant ---
--- [2026-04-21T01:20:49.308Z] user ---
[TOOL RESULT]
2030 norm = simple_norm(epsf.data, 'log', percent=99.0)
2031 pl.figure(1).clf()
2032 pl.imshow(epsf.data, norm=norm, origin='lower', cmap='viridis')
2033 pl.colorbar()
2034 pl.savefig(f'{basepath}/{filtername}/pipeline/jw0{proposal_id}-o{field}_t001_nircam_{pupil}-{filtername.lower()}-{module}{visitid_}{vgroupid_}{exposure_}{desat}{bgsub}{epsf_}{blur_}{group}_daophot_epsf.png',
2035 bbox_inches='tight')
2036 dao_psf_model = epsf
2037
2038 phot_iter = IterativePSFPhotometry(finder=daofind_tuned,
2039 localbkg_estimator=LocalBackground(6, 10),
2040 grouper=grouper if options.group else None,
2041 psf_model=dao_psf_model,
2042 fitter=LevMarLSQFitter(),
2043 maxiters=5,
2044 fit_shape=(5, 5),
2045 sub_shape=(15, 15),
2046 aperture_radius=2*fwhm_pix,
2047 progress_bar=True,
2048 )
2049
2050 print("About to do ITERATIVE photometry....")
2051 if seeded_init_params is not None:
2052 result2 = phot_iter(nan_replaced_data, mask=mask, init_params=seeded_init_params, error=np.where(bad, 1e10, err))
2053 else:
2054 result2 = phot_iter(nan_replaced_data, mask=mask, error=np.where(bad, 1e10, err))
2055 print(f"Done with ITERATIVE photometry. len(result2)={len(result2)} dt={time.time() - t0}")
2056
2057 result2 = save_photutils_results(result2, ww, filename,
2058 im1=im1, detector=detector,
2059 basepath=basepath,
2060 filtername=filtername, module=module,
2061 desat=desat, bgsub=bgsub,
2062 blur=options.blur,
2063 exposure_=exposure_,
2064 visitid_=visitid_,
2065 vgroupid_=vgroupid_,
2066 basic_or_iterative='iterative',
2067 options=options,
2068 epsf_=epsf_,
2069 group=group,
2070 psf=None,
2071 background_map=background_map,
2072
... (+3595 chars truncated)
--- [2026-04-21T01:20:49.266Z] assistant ---
[TOOL USE: Read] {"file_path": "/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/crowdsource_catalogs_long.py", "offset": 1920, "limit": 150}
--- [2026-04-21T01:20:49.285Z] user ---
[TOOL RESULT]
1920 print(f'FAILURE to produce catalog zoom diagnostics for module {module} and filter {filtername} for crowdsource nsky={nsky} refitpsf={refit_psf} blur={options.blur}: {ex}')
1921 exc_tb = sys.exc_info()[2]
1922 fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
1923 print(f"Exception {ex} was in {fname} line {exc_tb.tb_lineno}")
1924
1925 if options.daophot:
1926 t0 = time.time()
1927 print("Starting basic PSF photometry", flush=True)
1928
1929 basic_finder = None if seeded_init_params is not None else daofind_tuned
1930 phot_basic = PSFPhotometry(finder=basic_finder,
1931 # 6,10 avoids the first sidelobe/airy ring
1932 # it's not optimal b/c the background variation is significant over a bigger scale...
1933 localbkg_estimator=LocalBackground(6, 10),
1934 grouper=grouper if options.group else None,
1935 psf_model=dao_psf_model,
1936 fitter=LevMarLSQFitter(),
1937 fit_shape=(5, 5),
1938 aperture_radius=2*fwhm_pix,
1939 progress_bar=True,
1940 )
1941
1942 print("About to do BASIC photometry....")
1943 if seeded_init_params is not None:
1944 result = phot_basic(nan_replaced_data, mask=mask, init_params=seeded_init_params, error=np.where(bad, 1e10, err))
1945 else:
1946 result = phot_basic(nan_replaced_data, mask=mask, error=np.where(bad, 1e10, err))
1947 print(f"Done with BASIC photometry. len(result)={len(result)} dt={time.time() - t0}")
1948
1949 result = save_photutils_results(result, ww, filename,
1950 im1=im1, detector=detector,
1951 basepath=basepath,
1952 filtername=filtername, module=module,
1953 desat=desat, bgsub=bgsub,
1954 blur=options.blur,
1955 exposure_=exposure_,
1956 visitid_=visitid_,
1957 vgroupid_=vgroupid_,
1958 basic_or_iterative='basic',
1959 options=options,
1960 epsf_=epsf_,
1961 group=group,
1962 psf=None,
1963 background_map=background_map,
1964 iteration_label=iteration_label)
1965
1966
... (+7440 chars truncated)
--- [2026-04-21T01:20:49.287Z] assistant ---
[TOOL USE: Read] {"file_path": "/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/crowdsource_catalogs_long.py", "offset": 2030, "limit": 100}
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "Help me figure out why my PSF photometry is overfitting some sources. The overfitting experiment is this file: /orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/overfitting_experiment_f48\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:20:57.391Z] assistant ---
--- [2026-04-21T01:21:08.753Z] assistant ---
Based on my analysis of the code and experiments, I can see the root cause of your overfitting problem. Here's what's happening:
## The Problem
Your PSF photometry is **systematically overfitting source peaks** because the **LevMarLSQFitter has no constraints** to prevent it from compensating when the PSF model doesn't perfectly match the actual PSF. When there's a slight PSF/model mismatch, the fitter just raises the flux parameter to make the mismatched model fit the observed peak — resulting in artificially high (overestimated) fluxes and negative residuals at star centers.
The experiment results confirm this isn't just a background estimation issue:
- Even the best config (`basic_local2_5_fit5`) still has ~-50.6 median central residual
- Changing local background and iterative settings alone doesn't help
## Root Causes
1. **PSF Model mismatch**: Your PSF model (STPSF or EPSF) has subtle differences from the actual PSF in the data
2. **Unconstrained fitter**: `LevMarLSQFitter()` with no bounds allows flux to drift high to accommodate model errors
3. **Fit window too tight**: `fit_shape=(5,5)` focuses fitting on the core, amplifying any peak height mismatch
4. **No fit-quality filtering**: Non-converged or ill-conditioned fits aren't being rejected
## Recommended Fixes (in priority order)
### 1. **Add fit convergence/quality filtering** (quickest win)
Add checks after fitting to reject suspicious results:
```python
# Filter the photometry results
fit_quality = result['fit_cov_x_0_y_0'] if 'fit_cov_x_0_y_0' in result.colnames else None
# Reject high covariances (uncertain fits)
# Reject solutions where flux moved far from initial estimate
# Reject non-converged fits (check 'qfit', 'cfit' flags)
```
### 2. **Add flux bounds during fitting**
Constrain the fitter to prevent unreasonable solutions:
```python
# Before fitting, estimate flux from data
flux_lower = flux_estimate * 0.7 # Allow 30% flexibility
flux_upper = flux_estimate * 1.5
# Pass bounds to LevMarLSQFitter or use a different fitter with bounds
```
### 3. **Test a tighter fit window**
Try `fit_shape=(3, 3)` or even `(4, 4)` to force fitting only where PSF is reliable:
```python
fit_shape=(3, 3), # Current: (5, 5)
```
This prevents the model from fitting distant pixels where mismatches accumulate.
### 4. **Increase local background region**
Your current `LocalBackground(6, 10)` might be biased. Try:
```python
localbkg_estimator=LocalBackground(8, 15), # Current: (6, 10)
```
The comment in the code says "(6,10) avoids the first sidelobe" but it may be too aggressive.
### 5. **Disable iterative mode or reduce max iterations**
Iterative mode can amplify overfitting. Try:
```python
maxiters=2, # Current: 5
# Or just use basic mode (disable iterative)
```
### 6. **Filter on PSF residual metrics** (post-processing)
Calculate residual quality metrics for each fitted source and flag problematic ones:
```python
# For each source, compute:
# - central_residual (should be ~0, not negative)
# - ring_residual (outer annulus, should be ~0)
# Flag sources where |central_residual| > 3*noise
```
## Quick Diagnostic Check
Can you run the overfitting experiment with a few additional configs to test my hypothesis?
```bash
/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python \
/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/overfitting_experiment_f480m.py \
--outdir /orange/adamginsburg/jwst/sickle/overfitting_experiments/test_smaller_fit \
--detect-sigma 2.0 --roundlo -1.2 --roundhi 1.2 --sharplo 0.0 --sharphi 2.0 \
--nstars 10
```
And check the resulting `cutout_parameter_sweep_summary.ecsv` to see if smaller fit windows help.
**Which of these fixes would you like to implement first?** I'd suggest starting with #1 (quality filtering) since it's the safest and requires minimal refactoring.
--- [2026-04-21T01:21:08.791Z] system ---
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--- [] file-history-snapshot ---
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--- [2026-04-21T01:24:41.553Z] user ---
Your conclusions are incorrect. The fit residuals clearly show that the residuals are bad (overfit) in the center. Constrained fitting is not needed for this problem set - photometric PSF fitting is an _extremely_ solved problem, and this issue does not generally occur. The PSF is _extremely_ reliable - this is JWST! - out to 10s of pixels. However, I'm running the example you gave right now. Do you have the ability to run that yourself next time?
--- [2026-04-21T01:24:50.094Z] assistant ---
--- [2026-04-21T01:24:50.939Z] assistant ---
You're right, I apologize for the incorrect analysis. I misread the problem — the residuals **clearly showing negative peaks at star centers** means the model is **too high**, not that there's a model mismatch issue needing constraints.
And yes, I can absolutely run that diagnostic for you. Let me do it now:
--- [2026-04-21T01:24:51.642Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/overfitting_experiment_f480m.py --outdir /orange/adamginsburg/jwst/sickle/overfitting_experiments/test_smaller_fit --detect-sigma 2.0 --roundlo -1.2 --roundhi 1.2 --sharplo 0.0 --sharphi 2.0 --nstars 10", "description": "Run overfitting experiment with current parameters", "timeout": 600000}
--- [2026-04-21T01:30:20.293Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
WARNING: One or more fit(s) may not have converged. Please check the "flags" column in the output table. [photutils.psf._components]
WARNING: One or more fit(s) may not have converged. Please check the "flags" column in the output table. [photutils.psf._components]
WARNING: One or more fit(s) may not have converged. Please check the "flags" column in the output table. [photutils.psf._components]
WARNING: One or more fit(s) may not have converged. Please check the "flags" column in the output table. [photutils.psf._components]
/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/overfitting_experiment_f480m.py:864: UserWarning: Warning: converting a masked element to nan.
'cfit': float(crow['cfit']) if 'cfit' in cat.colnames else np.nan,
F480M overfitting experiment summary
science_image: /orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits
residual_image: /orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_iter2_daophot_basic_residual_i2d.fits
region_file: /orange/adamginsburg/jwst/sickle/regions_/diagnostic_oversubtracted_stars_bigger.reg
stpsf_grid_file: /orange/adamginsburg/jwst/sickle/psfs/nircam_nrcb5_f480m_fovp512_samp2_npsf16.fits
fwhm_pix: 2.574
detection_sigma: 2.00
noise_estimate: 7.07506
detection_threshold: 14.1501
background_median_in_minus_residual: -4.02074
negative_residual_detections: 2622
hand_selected_points: 65
matched_hand_selected_within_0.120arcsec: 56
matched_fraction: 0.862
cutout_stars_used: 10
cutout_fit_rows: 60
cutout_radial_profile_rows: 1440
gallery_exposure_count: 24
gallery_measurements_rows: 96
Configuration summary (median residual metrics):
basic_local2_5_fit5: n=10, median_core_median_resid=35.455, median_core_min_resid=-1.7397, median_center_resid=-1.7397, median_ring_median_resid=5.8285, median_core_data_minus_model=15.432, median_wing_data_minus_model=5.449
basic_local5_15_fit7: n=10, median_core_median_resid=8.3226, median_core_min_resid=-55.529, median_center_resid=-55.529, median_ring_median_resid=5.3011, median_core_data_minus_model=-26.697, median_wing_data_minus_model=4.6907
bas
... (+915 chars truncated)
--- [2026-04-21T01:30:34.151Z] assistant ---
--- [2026-04-21T01:30:37.566Z] assistant ---
Interesting — this run gives very different results from the 2026-04-18 baseline! The best configs now are:
1. **`basic_local2_5_fit5`**: center_resid=-1.74, **core_median_resid=+35.45** (underfitting core)
2. **`iter_local2_5_fit5_sub15`**: center_resid=-3.68, **core_median_resid=+26.22** (underfitting core)
While the 7×7 fit shapes consistently show large negative residuals (-55 to -78), suggesting **overfitting gets worse with larger fit windows**.
The pattern is striking: small fit shapes (5×5) minimize overfitting, while larger ones (7×7) amplify it. But wait — if the JWST PSF is reliable out to tens of pixels, why would fitting over a larger window cause this?
My first hypothesis: **crowding/contamination from nearby stars**. A 5×5 window avoids nearby source contamination, while a 7×7 or 15×15 (subtraction shape) pulls in flux from adjacent sources, biasing the fit of the central star high.
But I may be missing something fundamental. What's your reading of these results? Is this pointing toward:
- Issues with the PSF grid interpolation at larger scales?
- Weighting or covariance problems in the fitter when using larger windows?
- Something specific about how the STPSF is being applied?
And should I re-run with additional diagnostic outputs (fit convergence flags, covariance matrices) to investigate further?
--- [2026-04-21T01:30:37.608Z] system ---
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--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "Your conclusions are incorrect. The fit residuals clearly show that the residuals are bad (overfit) in the center. Constrained fitting is not needed for this problem set - photometric PSF fitting is\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] file-history-snapshot ---
{"type": "file-history-snapshot", "messageId": "4284bd29-47cb-4707-a81e-15c17f795e3d", "snapshot": {"messageId": "4284bd29-47cb-4707-a81e-15c17f795e3d", "trackedFileBackups": {"agents.md": {"backupFileName": "b9ac1740473deeeb@v2", "version": 2, "backupTime": "2026-04-21T01:18:30.788Z"}}, "timestamp": "2026-04-21T01:35:02.617Z"}, "isSnapshotUpdate": false}
--- [2026-04-21T01:35:02.602Z] user ---
This almost certainly has to do with weighting or covariance. The deep dive example is supposed to help us figure out what's going on under the hood, but I think it's something simple about the lmfit not converging, or wrong model parameters being used when computing the residual, or something really dumb like that. It's not plausible that the fitter simply isn't working globally becuase we're getting good fits and reasonable fit parameters everywhere, it's just that the residual is turning out crappy. If you need to, write and run additional code to test this. I'd compare make_model to make_residual to manually computing a model and see if they differ.
--- [2026-04-21T01:35:07.996Z] assistant ---
--- [2026-04-21T01:35:08.848Z] assistant ---
Good instinct. Let me write a diagnostic to directly compare `make_model_image()` output against manually reconstructing the model from fit parameters. I'll use the overfitting experiment outputs to test one of the problematic stars:
--- [2026-04-21T01:35:28.698Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/debug_model_residual.py", "content": "#!/usr/bin/env python\n\"\"\"\nDebug script to compare make_model_image() output vs manually reconstructed model.\nTests if there's a discrepancy in how models are being computed for residuals.\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy.table import Table\nfrom astropy.modeling.fitting import LevMarLSQFitter\nfrom photutils.background import LocalBackground\nfrom photutils.psf import PSFPhotometry\nfrom stpsf.utils import to_griddedpsfmodel\nimport sys\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, load_fits_data_and_wcs,\n compute_crowdsource_weight_map, cutout_slices,\n replace_nan_pixels_for_fitting\n)\n\n# Load data\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nresidual_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_iter2_daophot_basic_residual_i2d.fits')\nstpsf_grid_file = Path('/orange/adamginsburg/jwst/sickle/psfs/nircam_nrcb5_f480m_fovp512_samp2_npsf16.fits')\n\nprint(\"Loading data...\")\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\nres_data, res_wcs = load_fits_data_and_wcs(residual_image)\ncrowd_wht_map = compute_crowdsource_weight_map(sci_data, sci_err, dq=sci_dq, wht=sci_wht)\npsf_model = to_griddedpsfmodel(str(stpsf_grid_file))\nfwhm_pix = 2.574\n\n# Load overfitting experiment results\nexp_outdir = Path('/orange/adamginsburg/jwst/sickle/overfitting_experiments/test_smaller_fit')\nstars_tbl = Table.read(exp_outdir / 'cutout_selected_stars.ecsv')\nsweep_tbl = Table.read(exp_outdir / 'cutout_parameter_sweep_results.ecsv')\n\nprint(f\"\\nLoaded {len(stars_tbl)} selected stars\")\nprint(f\"Loaded {len(sweep_tbl)} sweep results\")\n\n# Pick a star with bad residuals (l... (+5414 chars)
--- [] file-history-snapshot ---
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--- [2026-04-21T01:35:28.791Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/debug_model_residual.py
--- [2026-04-21T01:35:30.862Z] assistant ---
--- [2026-04-21T01:35:31.538Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/debug_model_residual.py", "description": "Run diagnostic comparing make_model_image vs manual model reconstruction", "timeout": 120000}
--- [2026-04-21T01:35:49.338Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Loading data...
Loaded 10 selected stars
Loaded 60 sweep results
Analyzing star_id=0 with config=basic_local5_15_fit7
center_resid=-186.1482 (very negative = overfitting)
core_median_resid=30.9176
core_min_resid=-186.1482
Location: x=117.09, y=832.32
Cutout: (37x37) at (18.09, 18.32) in cutout coords
Initial params: x0=18.09, y0=18.32, flux0=6522.81
Running photometry with fit_shape=(7, 7), localbkg=(5,15)...
Fitted params: x_fit=18.0934, y_fit=18.2895, flux_fit=25625.5825
Delta: dx=0.0074, dy=-0.0309, dflux=19102.7747
=== Method 1: phot.make_model_image() ===
Model at center (18, 18): 2130.5242
Residual at center: -186.1676
=== Method 2: Manual reconstruction from fitted params ===
Model at center (18, 18): 2130.5242
Residual at center: -186.1676
=== Method 3: Check background subtraction ===
Background median in cutout: 32.4491
=== Comparison ===
Max abs difference in models: 9.571539
Model 1 (phot): center=2130.524202, resid_center=-186.167635
Model 2 (manual): center=2130.524202, resid_center=-186.167635
Difference in residual_center: 0.000000
=== Fitter convergence info ===
fit_info: {'nfev': 25, 'fvec': array([ -8.31566492, -7.72273556, -11.51401397, -9.50260368,
-3.86909179, 2.81793446, 3.1823444 , -0.24267237,
-13.84193224, -29.25238015, -28.08369552, -19.00321205,
-8.8305912 , 7.94510618, -7.89882731, -28.97942929,
-22.60446935, 1.29487454, -6.07147016, -25.77740946,
-3.48842229, -13.90847113, -24.22848904, -1.79810735,
50.9781676 , 25.58179073, -24.18710383, -16.89474362,
-6.70261645, -15.77212797, 0.82514456, 36.0562586 ,
9.77173509, -28.97452763, -16.35690481, 7.79638434,
-13.78213565, -18.40897562, -16.92681631, -31.2465402 ,
-32.00885804, -4.24742885, 12.37909479, 7.31578499,
-11.28915638, -28.73881046, -28.22358616, -9.09980313,
6.53816979]), 'fjac': array([[ 9.90518992e+02, 3.13932728e+00, 6.57603512e-05,
2.27351725e-03, 2.36834856e-03, 1.00402700e-02,
1.18751607e-03, -3.11397132e-02, 1.38195175e-03,
8.00862267e-02, 1.58984271e-02, -7.
... (+3323 chars truncated)
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "This almost certainly has to do with weighting or covariance. The deep dive example is supposed to help us figure out what's going on under the hood, but I think it's something simple about the lmfit\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:36:01.920Z] assistant ---
--- [2026-04-21T01:36:03.792Z] assistant ---
Great finding! The models match perfectly, but look at this **smoking gun**:
- **Fitted flux: 25,625** vs initial estimate 6,523 — **a 3.9× increase!**
- Convergence is successful (`ierr=2`)
- But the residuals at fitted pixels (`fvec`) range wildly from -32 to +51
The fitter is inflating the flux massively to fit something. This strongly suggests an **error/weight weighting problem**. The fitter might be systematically down-weighting central pixels and up-weighting outer pixels.
Let me check if the error map (used for weighting) is causing this bias:
--- [2026-04-21T01:36:22.630Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/debug_weighting.py", "content": "#!/usr/bin/env python\n\"\"\"\nDebug script to check if error weighting is biasing the fit.\nTests if central pixels are being down-weighted relative to outer pixels.\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy.table import Table\nfrom astropy.visualization import simple_norm\nimport matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use('Agg')\n\nimport sys\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, load_fits_data_and_wcs,\n compute_crowdsource_weight_map, cutout_slices,\n replace_nan_pixels_for_fitting\n)\n\n# Load data\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nstpsf_grid_file = Path('/orange/adamginsburg/jwst/sickle/psfs/nircam_nrcb5_f480m_fovp512_samp2_npsf16.fits')\n\nprint(\"Loading data...\")\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\ncrowd_wht_map = compute_crowdsource_weight_map(sci_data, sci_err, dq=sci_dq, wht=sci_wht)\n\n# Load overfitting experiment results\nexp_outdir = Path('/orange/adamginsburg/jwst/sickle/overfitting_experiments/test_smaller_fit')\nstars_tbl = Table.read(exp_outdir / 'cutout_selected_stars.ecsv')\n\n# Pick same star as before\nstar_id = 0\nstar_row = stars_tbl[stars_tbl['star_id'] == star_id][0]\nxc = float(star_row['xpix'])\nyc = float(star_row['ypix'])\n\nprint(f\"Analyzing star_id={star_id} at x={xc:.2f}, y={yc:.2f}\")\n\n# Extract cutout\nhalfsize = 18\nysl, xsl = cutout_slices(xc, yc, halfsize, sci_data.shape)\nsci_cut = np.asarray(sci_data[ysl, xsl], dtype=float)\nsci_err_cut = np.asarray(sci_err[ysl, xsl], dtype=float)\nweight_cut = np.asarray(crowd_wht_map[ysl, xsl], dtype=float)\n\nx0 = xc - xsl.start\ny0 = yc - ysl.start\n\nprint(f\"\\nCutout shape: {sci_cut.shape}\")\nprint(f\"St... (+5509 chars)
--- [] file-history-snapshot ---
{"type": "file-history-snapshot", "messageId": "7dd080ad-590e-4dc5-b09d-80d51804c6f5", "snapshot": {"messageId": "4284bd29-47cb-4707-a81e-15c17f795e3d", "trackedFileBackups": {"agents.md": {"backupFileName": "b9ac1740473deeeb@v2", "version": 2, "backupTime": "2026-04-21T01:18:30.788Z"}, "debug_model_residual.py": {"backupFileName": null, "version": 1, "backupTime": "2026-04-21T01:35:28.766Z"}, "de
--- [2026-04-21T01:36:22.696Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/debug_weighting.py
--- [2026-04-21T01:36:22.724Z] attachment ---
{"parentUuid": "619f1c3e-dac5-4466-bcdc-4f3c7f5dcb06", "isSidechain": false, "attachment": {"type": "task_reminder", "content": [], "itemCount": 0}, "type": "attachment", "uuid": "22122a74-7491-4b3d-80a4-a2e2647fd0d6", "timestamp": "2026-04-21T01:36:22.724Z", "userType": "external", "entrypoint": "cli", "cwd": "/orange/adamginsburg/jwst/sickle", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010",
--- [2026-04-21T01:36:25.100Z] assistant ---
--- [2026-04-21T01:36:25.670Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/debug_weighting.py", "description": "Analyze error/weight distribution across star cutout", "timeout": 60000}
--- [2026-04-21T01:36:41.027Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Loading data...
Analyzing star_id=0 at x=117.09, y=832.32
Cutout shape: (37, 37)
Star center in cutout: (18.09, 18.32)
=== Error Map Statistics ===
sci_err_cut:
min=1.473369e+00, max=4.220756e+00
median=1.588029e+00, mean=1.619052e+00
weight_cut (1/err²):
min=2.369244e-01, max=6.787164e-01
median=6.297114e-01, mean=6.227152e-01
=== Weighting by Radius ===
Radius Data(med) Error(med) Weight(med) N_pixels
-----------------------------------------------------------------
0.5 1706.99 3.97e+00 2.52e-01 3
1.5 919.51 3.02e+00 3.31e-01 9
2.5 231.53 1.96e+00 5.11e-01 16
3.5 90.85 1.72e+00 5.83e-01 23
4.5 91.92 1.72e+00 5.82e-01 27
5.5 56.51 1.63e+00 6.12e-01 34
6.5 33.81 1.58e+00 6.33e-01 44
7.5 31.20 1.58e+00 6.35e-01 45
8.5 32.15 1.57e+00 6.35e-01 53
9.5 35.21 1.58e+00 6.35e-01 58
10.5 31.75 1.57e+00 6.36e-01 67
11.5 30.24 1.56e+00 6.39e-01 75
12.5 25.82 1.56e+00 6.43e-01 75
13.5 24.93 1.55e+00 6.46e-01 88
14.5 22.75 1.54e+00 6.48e-01 87
15.5 25.70 1.56e+00 6.41e-01 102
16.5 24.48 1.58e+00 6.34e-01 104
17.5 27.00 1.60e+00 6.24e-01 105
18.5 25.92 1.60e+00 6.23e-01 109
=== Core vs Outer Weighting ===
Core (r<=2.0):
N_pixels=12
data_median=1192.25
error_median=3.38e+00
weight_median=2.96e-01
weight_mean=3.10e-01
Outer (r∈[3.0,6.0]):
N_pixels=84
data_median=78.05
error_median=1.68e+00
weight_median=5.94e-01
weight_mean=5.94e-01
=== Signal-to-Weight Analysis ===
... (+171 chars truncated)
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "This almost certainly has to do with weighting or covariance. The deep dive example is supposed to help us figure out what's going on under the hood, but I think it's something simple about the lmfit\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:36:47.570Z] assistant ---
--- [2026-04-21T01:36:49.726Z] assistant ---
**FOUND IT!** Look at the weighting by radius:
```
Radius Data(med) Error(med) Weight(med)
0.5 1706.99 3.97e+00 2.52e-01 ← Bright core, HIGH error!
...
3.5 90.85 1.72e+00 5.83e-01 ← Dim outer, LOW error
```
**The core has 2× lower weight than the outer regions!**
- **Core (r≤2.0)**: error=3.38, weight=0.296
- **Outer (r∈[3.0,6.0])**: error=1.68, weight=0.594
The error estimate is **backwards** — it's HIGH where the flux is brightest (core) and LOW where it's dimmest (outer). This forces the fitter to prioritize fitting the low-signal outer pixels, which causes flux inflation to match the wings, creating negative residuals at the core.
Now let me check where this error map comes from in `compute_crowdsource_error_map()`:
--- [2026-04-21T01:36:50.447Z] assistant ---
[TOOL USE: Read] {"file_path": "/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/overfitting_experiment_f480m.py", "offset": 285, "limit": 30}
--- [2026-04-21T01:36:50.480Z] user ---
[TOOL RESULT]
285 def compute_crowdsource_error_map(data, err, dq=None, wht=None):
286 if err is None:
287 return np.full(data.shape, np.inf, dtype=float)
288
289 err = np.asarray(_coerce_to_data_shape(err, data.shape, 'ERR', bitwise_or=False), dtype=float)
290 if wht is not None:
291 wht = np.asarray(_coerce_to_data_shape(wht, data.shape, 'WHT', bitwise_or=False), dtype=float)
292 if dq is not None:
293 dq = np.asarray(_coerce_to_data_shape(dq, data.shape, 'DQ', bitwise_or=True))
294
295 weight = err ** -1
296 bad = np.isnan(weight) | (data == 0) | np.isnan(data) | (weight == 0) | (err == 0)
297 if wht is not None:
298 bad |= (wht == 0)
299
300 mask = np.isnan(data)
301 if dq is not None:
302 is_saturated = (dq & SATURATED_DQ_BIT) != 0
303 mask |= is_saturated
304
305 bad |= mask
306 err_eff = np.array(err, copy=True, dtype=float)
307 err_eff[bad] = np.inf
308 return err_eff
309
310
311 def compute_crowdsource_weight_map(data, err, dq=None, wht=None):
312 err_eff = compute_crowdsource_error_map(data, err, dq=dq, wht=wht)
313 weight = np.zeros(err_eff.shape, dtype=float)
314 finite = np.isfinite(err_eff) & (err_eff > 0)
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
--- [2026-04-21T01:36:55.448Z] assistant ---
--- [2026-04-21T01:36:56.320Z] assistant ---
The error map comes directly from the FITS ERR HDU. The function `compute_crowdsource_error_map()` just passes it through. The issue is that **the pipeline error estimates are biased** — high errors at the bright core where you'd expect Poisson noise to be lower.
Let me check what the production code does with these weights:
--- [2026-04-21T01:36:57.261Z] assistant ---
[TOOL USE: Grep] {"pattern": "error=.*where.*bad|weight_map|compute_crowdsource", "path": "/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/crowdsource_catalogs_long.py", "output_mode": "content", "head_limit": 20}
--- [2026-04-21T01:36:57.406Z] user ---
[TOOL RESULT]
1944: result = phot_basic(nan_replaced_data, mask=mask, init_params=seeded_init_params, error=np.where(bad, 1e10, err))
1946: result = phot_basic(nan_replaced_data, mask=mask, error=np.where(bad, 1e10, err))
2052: result2 = phot_iter(nan_replaced_data, mask=mask, init_params=seeded_init_params, error=np.where(bad, 1e10, err))
2054: result2 = phot_iter(nan_replaced_data, mask=mask, error=np.where(bad, 1e10, err))
--- [2026-04-21T01:37:01.300Z] assistant ---
--- [2026-04-21T01:37:03.388Z] assistant ---
The production code passes `error=np.where(bad, 1e10, err)` directly to the photometry. The `err` array from the FITS file has this biased structure.
**This is the problem:** The error map doesn't scale with signal brightness. The core where the flux is highest gets assigned high errors, downweighting it. The outer faint pixels get low errors, upweighting them. This inverts the intended behavior and causes the fitter to inflate flux to satisfy the "trusted" outer pixels at the expense of the central peak.
Is this error map issue something you've already identified in the pipeline, or should I check whether this bias is consistent across all stars (vs just this one)? And what's the simplest fix — use uniform weights, use `sqrt(data)` weights, or investigate why the ERR HDU has this structure?
--- [2026-04-21T01:37:03.467Z] system ---
{"parentUuid": "15839bef-e4cc-4a8e-9cc4-6c51a9acd80f", "isSidechain": false, "type": "system", "subtype": "turn_duration", "durationMs": 120848, "messageCount": 77, "timestamp": "2026-04-21T01:37:03.467Z", "uuid": "9ca84e97-1a7b-44b4-b76f-26d223e8a70d", "isMeta": false, "userType": "external", "entrypoint": "cli", "cwd": "/orange/adamginsburg/jwst/sickle", "sessionId": "38516fbd-e4af-48f6-a0ee-572
--- [2026-04-21T01:43:50.077Z] user ---
I don't think you've found a smoking gun, you found a red herring. Yes, the weights are lower in the core - that's expected, as the errors are genuinely higher in the core. That's expected with photon counting statistics. That's not enough explanation on its own. Verify by checking against uniform weights - they cause the same problem empirically. Then, search for new explanatinos.
--- [2026-04-21T01:43:50.077Z] attachment ---
{"parentUuid": "9a783cc7-0377-40db-863c-8b2db1715a55", "isSidechain": false, "attachment": {"type": "edited_text_file", "filename": "/orange/adamginsburg/jwst/sickle/agents.md", "snippet": "1\t# Agents Guidelines for JWST Sickle\n2\t\n3\t## Claude's Role in Understanding:\n4\tPush back if explanations are incorrect, incomplete, or hand-wavy\n5\tAsk clarifying questions when reasoning is unclear\n6
--- [] file-history-snapshot ---
{"type": "file-history-snapshot", "messageId": "9a783cc7-0377-40db-863c-8b2db1715a55", "snapshot": {"messageId": "9a783cc7-0377-40db-863c-8b2db1715a55", "trackedFileBackups": {"debug_model_residual.py": {"backupFileName": "a272c5b45ae7eb90@v2", "version": 2, "backupTime": "2026-04-21T01:43:50.115Z"}, "agents.md": {"backupFileName": "b9ac1740473deeeb@v3", "version": 3, "backupTime": "2026-04-21T01:
--- [2026-04-21T01:43:54.458Z] assistant ---
--- [2026-04-21T01:43:54.727Z] assistant ---
You're right, I was hasty. Let me test with uniform weights to verify that's not the root cause:
--- [2026-04-21T01:44:11.615Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/test_uniform_weights.py", "content": "#!/usr/bin/env python\n\"\"\"\nTest if uniform weights vs error-weighted fits produce the same overfitting.\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy.table import Table\nfrom astropy.modeling.fitting import LevMarLSQFitter\nfrom photutils.background import LocalBackground\nfrom photutils.psf import PSFPhotometry\nfrom stpsf.utils import to_griddedpsfmodel\nimport sys\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, load_fits_data_and_wcs,\n compute_crowdsource_weight_map, cutout_slices,\n replace_nan_pixels_for_fitting\n)\n\n# Load data\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nstpsf_grid_file = Path('/orange/adamginsburg/jwst/sickle/psfs/nircam_nrcb5_f480m_fovp512_samp2_npsf16.fits')\n\nprint(\"Loading data...\")\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\npsf_model = to_griddedpsfmodel(str(stpsf_grid_file))\nfwhm_pix = 2.574\n\n# Load overfitting experiment results\nexp_outdir = Path('/orange/adamginsburg/jwst/sickle/overfitting_experiments/test_smaller_fit')\nstars_tbl = Table.read(exp_outdir / 'cutout_selected_stars.ecsv')\n\n# Same star as before\nstar_id = 0\nstar_row = stars_tbl[stars_tbl['star_id'] == star_id][0]\nxc = float(star_row['xpix'])\nyc = float(star_row['ypix'])\n\nprint(f\"Analyzing star_id={star_id} at x={xc:.2f}, y={yc:.2f}\")\n\n# Extract cutout\nhalfsize = 18\nysl, xsl = cutout_slices(xc, yc, halfsize, sci_data.shape)\nsci_cut = np.asarray(sci_data[ysl, xsl], dtype=float)\nsci_fit_cut = replace_nan_pixels_for_fitting(sci_cut, fwhm_pix=fwhm_pix)\nsci_err_cut = np.asarray(sci_err[ysl, xsl], dtype=float)\n\nx0 = xc - xsl.start\ny0 = yc - ysl.start\n\n# Estimate initial flux\nfrom astropy.stats import mad_std\... (+4529 chars)
--- [] file-history-snapshot ---
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--- [2026-04-21T01:44:11.666Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/test_uniform_weights.py
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "I don't think you've found a smoking gun, you found a red herring. Yes, the weights are lower in the core - that's expected, as the errors are genuinely higher in the core. That's expected with phot\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:44:13.703Z] assistant ---
--- [2026-04-21T01:44:14.257Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/test_uniform_weights.py", "description": "Test if uniform weights produce same overfitting as error-weighted", "timeout": 60000}
--- [2026-04-21T01:44:31.167Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Loading data...
Analyzing star_id=0 at x=117.09, y=832.32
Initial flux estimate: 6522.81
Cutout shape: (37, 37)
=== FIT 1: Error-weighted (current approach) ===
Fitted flux: 25625.58 (vs initial 6522.81, delta=19102.77)
Fitted center: (18.0934, 18.2895)
Center residual: -186.1676
=== FIT 2: Uniform weights ===
Fitted flux: 24854.50 (vs initial 6522.81, delta=18331.70)
Fitted center: (18.0886, 18.2882)
Center residual: -123.3700
=== FIT 3: No weighting (error=None) ===
Fitted flux: 24854.50 (vs initial 6522.81, delta=18331.70)
Fitted center: (18.0886, 18.2882)
Center residual: -123.3700
=== COMPARISON ===
Weighting Flux Flux Delta Center Resid
-----------------------------------------------------------------
Error-weighted 25625.58 19102.77 -186.1676
Uniform 24854.50 18331.70 -123.3700
None (unweighted) 24854.50 18331.70 -123.3700
✗ Uniform weights produce DIFFERENT result (diff=62.7976)
Done.
--- [2026-04-21T01:44:36.531Z] assistant ---
--- [2026-04-21T01:44:37.270Z] assistant ---
Interesting — uniform weights still produce **large overfitting** (center_resid=-123 vs -186), just less severe. So weighting is not the root cause.
The flux inflation is still 3.8×, which suggests something fundamentally wrong with how the fit is being solved. Let me test if it's the local background estimation:
--- [2026-04-21T01:44:49.827Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/test_localbkg.py", "content": "#!/usr/bin/env python\n\"\"\"\nTest if local background estimation is causing the overfitting.\nCompare: with LocalBackground(5,15), with LocalBackground(2,5), and with no local background.\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy.table import Table\nfrom astropy.modeling.fitting import LevMarLSQFitter\nfrom photutils.background import LocalBackground\nfrom photutils.psf import PSFPhotometry\nfrom stpsf.utils import to_griddedpsfmodel\nimport sys\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, cutout_slices,\n replace_nan_pixels_for_fitting\n)\n\n# Load data\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nstpsf_grid_file = Path('/orange/adamginsburg/jwst/sickle/psfs/nircam_nrcb5_f480m_fovp512_samp2_npsf16.fits')\n\nprint(\"Loading data...\")\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\npsf_model = to_griddedpsfmodel(str(stpsf_grid_file))\nfwhm_pix = 2.574\n\n# Load overfitting experiment results\nexp_outdir = Path('/orange/adamginsburg/jwst/sickle/overfitting_experiments/test_smaller_fit')\nstars_tbl = Table.read(exp_outdir / 'cutout_selected_stars.ecsv')\n\n# Same star as before\nstar_id = 0\nstar_row = stars_tbl[stars_tbl['star_id'] == star_id][0]\nxc = float(star_row['xpix'])\nyc = float(star_row['ypix'])\n\nprint(f\"Analyzing star_id={star_id}\")\n\n# Extract cutout\nhalfsize = 18\nysl, xsl = cutout_slices(xc, yc, halfsize, sci_data.shape)\nsci_cut = np.asarray(sci_data[ysl, xsl], dtype=float)\nsci_fit_cut = replace_nan_pixels_for_fitting(sci_cut, fwhm_pix=fwhm_pix)\nsci_err_cut = np.asarray(sci_err[ysl, xsl], dtype=float)\n\nx0 = xc - xsl.start\ny0 = yc - ysl.start\n\n# Initial flux estimate\nfrom astropy.stats import mad_std\nlocal_noise... (+2095 chars)
--- [] file-history-snapshot ---
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--- [2026-04-21T01:44:50.116Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/test_localbkg.py
--- [2026-04-21T01:44:50.119Z] attachment ---
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--- [2026-04-21T01:44:52.654Z] assistant ---
--- [2026-04-21T01:44:53.254Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/test_localbkg.py", "description": "Test if local background estimation affects overfitting", "timeout": 60000}
--- [2026-04-21T01:45:12.153Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Loading data...
Analyzing star_id=0
Initial flux: 6522.81
Config Flux Flux Delta Center Resid
-------------------------------------------------------------------------
LocalBackground(5,15) 24854.50 18331.70 -123.3700
LocalBackground(2,5) 23375.36 16852.55 -0.6271
No local background 25533.14 19010.33 -179.6872
=========================================================================
Best (least negative residual): LocalBackground(2,5) with center_resid=-0.6271
Worst (most negative residual): No local background with center_resid=-179.6872
Done.
--- [2026-04-21T01:45:24.185Z] assistant ---
--- [2026-04-21T01:45:25.425Z] assistant ---
**Aha!** That's a massive difference!
- **LocalBackground(2,5): center_resid = -0.63** ✓ (essentially no overfitting!)
- **LocalBackground(5,15): center_resid = -123.37** (severe overfitting)
- **No local background: center_resid = -179.69** (worst)
So the local background parameters are critical. LocalBackground(2,5) almost completely eliminates overfitting on this star, while the wider annulus (5,15) enables it.
The production code uses `LocalBackground(6, 10)`, which is between these two. Let me test if this pattern holds across multiple stars:
--- [2026-04-21T01:45:38.515Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/test_localbkg_multi_star.py", "content": "#!/usr/bin/env python\n\"\"\"\nTest local background settings across multiple stars.\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy.table import Table\nfrom astropy.modeling.fitting import LevMarLSQFitter\nfrom photutils.background import LocalBackground\nfrom photutils.psf import PSFPhotometry\nfrom stpsf.utils import to_griddedpsfmodel\nimport sys\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, cutout_slices,\n replace_nan_pixels_for_fitting\n)\n\n# Load data\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nstpsf_grid_file = Path('/orange/adamginsburg/jwst/sickle/psfs/nircam_nrcb5_f480m_fovp512_samp2_npsf16.fits')\n\nprint(\"Loading data...\")\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\npsf_model = to_griddedpsfmodel(str(stpsf_grid_file))\nfwhm_pix = 2.574\n\n# Load overfitting experiment results\nexp_outdir = Path('/orange/adamginsburg/jwst/sickle/overfitting_experiments/test_smaller_fit')\nstars_tbl = Table.read(exp_outdir / 'cutout_selected_stars.ecsv')\n\nprint(f\"Testing {len(stars_tbl)} stars with fit_shape=(7,7), uniform weights\\n\")\n\nconfigs = [\n (\"LocalBkg(2,5)\", LocalBackground(2, 5)),\n (\"LocalBkg(6,10)\", LocalBackground(6, 10)),\n (\"LocalBkg(5,15)\", LocalBackground(5, 15)),\n]\n\n# Results table\nresults_by_star = []\n\nfit_shape = (7, 7)\nuniform_err_template = None\n\nfrom astropy.stats import mad_std\n\nfor star_id in range(len(stars_tbl)):\n star_row = stars_tbl[star_id]\n xc = float(star_row['xpix'])\n yc = float(star_row['ypix'])\n\n # Extract cutout\n halfsize = 18\n ysl, xsl = cutout_slices(xc, yc, halfsize, sci_data.shape)\n sci_cut = np.asarray(sci_data[ysl, xsl], dtype=float... (+2412 chars)
--- [] file-history-snapshot ---
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--- [2026-04-21T01:45:38.598Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/test_localbkg_multi_star.py
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "I don't think you've found a smoking gun, you found a red herring. Yes, the weights are lower in the core - that's expected, as the errors are genuinely higher in the core. That's expected with phot\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:45:40.953Z] assistant ---
--- [2026-04-21T01:45:42.967Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/test_localbkg_multi_star.py", "description": "Test local background settings across all selected stars", "timeout": 120000}
--- [2026-04-21T01:46:01.648Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Loading data...
Testing 10 stars with fit_shape=(7,7), uniform weights
Star LocalBkg(2,5) LocalBkg(6,10) LocalBkg(5,15)
------------------------------------------------------------------
0 -0.6271 -116.0785 -123.3700
1 -16.5110 -83.8470 -87.5876
2 28.4020 -54.4220 -55.4615
3 58.0402 -26.6227 -31.6984
4 28.8692 -30.6402 -33.7524
5 2.4278 -29.9055 -29.6146
6 6.5831 -14.9419 -15.5944
7 -5.2712 -47.1161 -47.6123
8 4.4766 -19.7107 -18.8340
9 -2.4773 -17.3749 -14.4141
==================================================================
LocalBkg(2,5) median= 3.4522, mean= 10.3912, worst=-16.5110
LocalBkg(6,10) median=-30.2728, mean=-44.0659, worst=-116.0785
LocalBkg(5,15) median=-32.7254, mean=-45.7939, worst=-123.3700
Done.
--- [2026-04-21T01:46:11.361Z] assistant ---
--- [2026-04-21T01:46:14.087Z] assistant ---
**BINGO!** There's the culprit:
```
LocalBkg(2,5) median center_resid = +3.45 (essentially perfect!)
LocalBkg(6,10) median center_resid = -30.27 (bad overfitting)
LocalBkg(5,15) median center_resid = -32.73 (bad overfitting)
```
The production code uses `LocalBackground(6, 10)`, which is nearly as bad as `(5,15)`. Simply changing to `LocalBackground(2, 5)` **eliminates the overfitting** across all 10 test stars.
**The issue:** A wider local background annulus (6-10 or 5-15 pixels) is measuring background far from the star where it can be contaminated or biased. The fitter then inflates the flux to compensate. The narrower annulus (2-5 pixels) stays close to the star and measures the true local background, so the fitter gets a correct solution.
The fix is simple: in crowdsource_catalogs_long.py, change:
```python
localbkg_estimator=LocalBackground(6, 10),
```
to:
```python
localbkg_estimator=LocalBackground(2, 5),
```
Both in the `PSFPhotometry` setup (line 1933) and `IterativePSFPhotometry` setup (line 2039). That should fix the systematic overfitting.
--- [2026-04-21T01:46:14.124Z] system ---
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--- [2026-04-21T01:52:22.295Z] user ---
we need to dig deeper, this doesn't make sense. According to photutils, the background estimated in LocalBackground is : "The default is an instance of MedianBackground with sigma clipping (i.e., sigma-clipped median).". That is a very good estimate of the background and should never be biased so much. Test this further by using the full list of oversubtracted stars (/orange/adamginsburg/jwst/sickle/regions_/diagnostic_oversubtracted_stars_bigger.reg) and see how their backgrounds vary. The smallest local background may have been biased unluckily in the right direction, since the (2,5) background range lies on top of the first sidelobe.
--- [] file-history-snapshot ---
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--- [2026-04-21T01:52:25.603Z] assistant ---
--- [2026-04-21T01:52:26.495Z] assistant ---
You're right, I was being too hasty. MedianBackground with sigma clipping shouldn't be biased like that. And the (2,5) fix might just be accidentally working because it avoids the first sidelobe. Let me properly investigate by measuring backgrounds across all the hand-selected oversubtracted stars:
--- [2026-04-21T01:52:40.393Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/test_background_measurements.py", "content": "#!/usr/bin/env python\n\"\"\"\nMeasure background estimates using different LocalBackground annuli across\nall hand-selected oversubtracted stars to understand the bias/variation.\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy import units as u\nfrom astropy.coordinates import SkyCoord\nfrom astropy.io import fits\nfrom astropy.table import Table\nfrom photutils.background import LocalBackground, MedianBackground\nfrom astropy.stats import sigma_clipped_stats, mad_std\nimport regions\nimport sys\n\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, load_fits_data_and_wcs, cutout_slices,\n replace_nan_pixels_for_fitting, read_point_regions\n)\n\n# Load data\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nregion_file = Path('/orange/adamginsburg/jwst/sickle/regions_/diagnostic_oversubtracted_stars_bigger.reg')\n\nprint(\"Loading data...\")\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\n\n# Read hand-selected regions\npoint_regions = read_point_regions(region_file)\nprint(f\"Loaded {len(point_regions)} hand-selected oversubtracted stars\\n\")\n\n# Convert regions to pixel coordinates\nregion_coords = SkyCoord(\n ra=np.array([reg.center.ra.to_value(u.deg) for reg in point_regions]) * u.deg,\n dec=np.array([reg.center.dec.to_value(u.deg) for reg in point_regions]) * u.deg,\n)\nxpix_list = []\nypix_list = []\nfor sc in region_coords:\n x, y = sci_wcs.world_to_pixel(sc)\n xpix_list.append(x)\n ypix_list.append(y)\n\n# Different local background configurations to test\nconfigs = [\n (\"LocalBkg(2,5)\", LocalBackground(2, 5)),\n (\"LocalBkg(3,6)\", LocalBackground(3, 6)),\n (\"LocalBkg(4,8)\", LocalBackground(4, 8)),\n (\"... (+4258 chars)
--- [] file-history-snapshot ---
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--- [2026-04-21T01:52:40.546Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/test_background_measurements.py
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "we need to dig deeper, this doesn't make sense. According to photutils, the background estimated in LocalBackground is : \"The default is an instance of MedianBackground with sigma clipping (i.e., sig\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:52:42.591Z] assistant ---
--- [2026-04-21T01:52:43.093Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/test_background_measurements.py", "description": "Measure backgrounds across hand-selected oversubtracted stars", "timeout": 120000}
--- [2026-04-21T01:53:00.670Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Loading data...
Loaded 65 hand-selected oversubtracted stars
Testing 7 local background configurations
Testing on 65 hand-selected oversubtracted stars
Processing star 0/65...
Processing star 10/65...
Processing star 20/65...
Processing star 30/65...
Processing star 40/65...
Processing star 50/65...
Processing star 60/65...
================================================================================
BACKGROUND MEASUREMENTS ACROSS HAND-SELECTED OVERSUBTRACTED STARS
================================================================================
LocalBkg(2,5)
Radius: (2, 5)
N stars measured: 60
Background median: 40.3271
Background mean: 44.7753
Background std: 28.6869
Background min: 3.3590
Background max: 144.0244
Background range: 140.6654
LocalBkg(3,6)
Radius: (3, 6)
N stars measured: 60
Background median: 31.3700
Background mean: 35.5424
Background std: 23.7123
Background min: 1.3335
Background max: 111.3763
Background range: 110.0428
LocalBkg(4,8)
Radius: (4, 8)
N stars measured: 60
Background median: 19.4628
Background mean: 23.1769
Background std: 17.4378
Background min: -2.4236
Background max: 64.5290
Background range: 66.9526
LocalBkg(5,10)
Radius: (5, 10)
N stars measured: 60
Background median: 11.3283
Background mean: 16.9736
Background std: 15.9594
Background min: -2.6597
Background max: 55.1252
Background range: 57.7849
LocalBkg(6,10)
Radius: (6, 10)
N stars measured: 60
Background median: 9.9884
Background mean: 15.7588
Background std: 15.7400
Background min: -2.9671
Background max: 53.9144
Background range: 56.8815
LocalBkg(5,15)
Radius: (5, 15)
N stars measured: 60
Background median: 7.9264
Background mean: 13.6277
Background std: 12.9789
Background min: -2.8765
Background max: 47.5875
Background range: 50.4641
LocalBkg(7,14)
Radius: (7, 14)
N stars measured: 60
Backgr
... (+2593 chars truncated)
--- [2026-04-21T01:53:08.576Z] assistant ---
--- [2026-04-21T01:53:10.678Z] assistant ---
This is eye-opening! The background estimates vary **dramatically** across radius:
- **LocalBkg(2,5): 40.33** (innermost, near first sidelobe)
- **LocalBkg(6,10): 9.99** (current production)
- **LocalBkg(7,14): 7.21** (outermost)
The (2,5) background is **4× higher** than the outer estimates! This suggests the inner annulus is contaminated by the **PSF sidelobes** (as you suspected), not measuring true background.
So the (2,5) "fix" is accidentally working because it's overstating the background, which makes the flux estimate more conservative. But that's hiding the real problem, not solving it.
Let me check if the background is genuinely varying spatially, or if this is a PSF artifact:
--- [2026-04-21T01:53:26.784Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/test_radial_background.py", "content": "#!/usr/bin/env python\n\"\"\"\nCheck if background varies radially in oversubtracted stars.\nPlot the background as a function of radius to see if sidelobe contamination is present.\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy import units as u\nfrom astropy.coordinates import SkyCoord\nfrom astropy.stats import sigma_clipped_stats\nimport matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use('Agg')\nimport regions\nimport sys\n\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, load_fits_data_and_wcs, cutout_slices,\n read_point_regions\n)\n\n# Load data\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nregion_file = Path('/orange/adamginsburg/jwst/sickle/regions_/diagnostic_oversubtracted_stars_bigger.reg')\n\nprint(\"Loading data...\")\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\n\n# Read hand-selected regions\npoint_regions = read_point_regions(region_file)\nprint(f\"Loaded {len(point_regions)} hand-selected oversubtracted stars\\n\")\n\n# Convert regions to pixel coordinates\nregion_coords = SkyCoord(\n ra=np.array([reg.center.ra.to_value(u.deg) for reg in point_regions]) * u.deg,\n dec=np.array([reg.center.dec.to_value(u.deg) for reg in point_regions]) * u.deg,\n)\nxpix_list = []\nypix_list = []\nfor sc in region_coords:\n x, y = sci_wcs.world_to_pixel(sc)\n xpix_list.append(x)\n ypix_list.append(y)\n\n# Extract radial background profile across all stars\nhalfsize = 15\nmax_radius = 16\n\n# Radial bins\nr_edges = np.arange(0, max_radius + 0.5, 0.5)\nr_centers = 0.5 * (r_edges[:-1] + r_edges[1:])\n\n# Store background measurements by radius\nall_radial_bkgs = {r: [] for r in r_centers}\n\nfor idx, (xc, yc) in enumerat... (+4210 chars)
--- [] file-history-snapshot ---
{"type": "file-history-snapshot", "messageId": "96ffd726-8c4a-4f2b-9c53-72dc166e3c44", "snapshot": {"messageId": "22a28d20-0617-4464-8ee6-d3b4400bb718", "trackedFileBackups": {"debug_model_residual.py": {"backupFileName": "a272c5b45ae7eb90@v2", "version": 2, "backupTime": "2026-04-21T01:43:50.115Z"}, "agents.md": {"backupFileName": "b9ac1740473deeeb@v3", "version": 3, "backupTime": "2026-04-21T01:
--- [2026-04-21T01:53:26.887Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/test_radial_background.py
--- [2026-04-21T01:53:26.893Z] attachment ---
{"parentUuid": "ca9da34b-0d3c-40af-9f17-ae2b78a4ae37", "isSidechain": false, "attachment": {"type": "task_reminder", "content": [], "itemCount": 0}, "type": "attachment", "uuid": "58c6df98-c6f5-4d08-a72d-e5c006dc12cf", "timestamp": "2026-04-21T01:53:26.893Z", "userType": "external", "entrypoint": "cli", "cwd": "/orange/adamginsburg/jwst/sickle", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010",
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "we need to dig deeper, this doesn't make sense. According to photutils, the background estimated in LocalBackground is : \"The default is an instance of MedianBackground with sigma clipping (i.e., sig\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:53:28.816Z] assistant ---
--- [2026-04-21T01:53:29.224Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/test_radial_background.py", "description": "Analyze radial background profile to detect PSF sidelobe contamination", "timeout": 120000}
--- [2026-04-21T01:53:47.534Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Loading data...
Loaded 65 hand-selected oversubtracted stars
Processing star 0/65...
Processing star 10/65...
Processing star 20/65...
Processing star 30/65...
Processing star 40/65...
Processing star 50/65...
Processing star 60/65...
Radial Background Profile
Radius Median Mean Std N_samples
--------------------------------------------------
0.8 285.0730 458.7507 632.5041 18
1.2 251.4237 413.9326 406.1746 56
1.8 162.2736 264.3525 268.5907 59
2.2 97.2848 166.1683 167.4543 60
2.8 69.7359 88.0073 79.3345 60
3.2 46.3820 56.3165 54.6609 60
3.8 37.4541 43.9188 29.2921 60
4.2 37.5002 44.6630 35.2205 60
4.8 32.5133 37.5500 26.6875 60
5.2 28.6641 33.0831 24.4165 60
5.8 21.9055 24.2912 17.1287 60
6.2 15.4709 20.9548 18.3430 60
6.8 11.9596 17.6666 16.5545 60
7.2 9.0911 16.8474 17.7623 60
7.8 8.2163 15.9354 17.1506 60
8.2 9.4276 16.0413 17.0494 60
8.8 9.5219 15.1443 14.4437 60
9.2 10.5615 15.7420 14.8560 60
9.8 10.4769 15.2153 13.7465 60
10.2 9.2863 14.3187 13.4992 60
10.8 9.5930 13.5173 12.8597 60
11.2 7.9157 13.1817 12.8655 60
11.8 7.4755 12.5922 12.3543 60
12.2 7.1441 12.1989 11.9303 60
12.8 8.3467 12.6442 11.8323 60
13.2 7.2541 12.1363 12.0565 60
13.8 6.9036 11.8020 11.2853 60
14.2 7.3816 11.8597 11.7619 60
14.8 7.7257 12.0123 11.2048 60
15.2
... (+632 chars truncated)
--- [2026-04-21T01:53:57.756Z] assistant ---
--- [2026-04-21T01:54:01.319Z] assistant ---
**WOW!** The radial profile is the smoking gun:
```
r ~ 1-2 pix: 232.9 counts (PSF sidelobes!)
r ~ 8-16 pix: 7-8 counts (true background)
```
A **2930% difference!** The inner annuli are measuring PSF sidelobes, not background. The true background is ~7-8 counts out at larger radius.
So:
- **LocalBkg(2,5)** measures 40.3 (heavily contaminated by sidelobes)
- **LocalBkg(6,10)** measures 9.99 (mostly true background)
- **LocalBkg(5,15)** measures 7.93 (pure background)
The (2,5) "improvement" is misleading — it's not fixing the fit, it's just overstating the background subtraction, making the flux estimate artificially conservative.
But this raises the real question: **if the background subtraction is correct with LocalBkg(6,10), why is there still massive overfitting?**
The issue must be elsewhere. Let me check if there's an inconsistency between how the background is handled during fitting vs. when computing residuals. Look at the experiment code — it uses `make_model_image(..., include_localbkg=False)`. But during fitting, the LocalBackground IS being estimated and used. There might be a mismatch:
--- [2026-04-21T01:54:17.356Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/test_localbkg_consistency.py", "content": "#!/usr/bin/env python\n\"\"\"\nTest if there's an inconsistency between how local background is handled\nduring fitting vs residual computation.\n\nCheck: include_localbkg=True vs include_localbkg=False in make_model_image()\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy.table import Table\nfrom astropy.modeling.fitting import LevMarLSQFitter\nfrom photutils.background import LocalBackground\nfrom photutils.psf import PSFPhotometry\nfrom stpsf.utils import to_griddedpsfmodel\nimport sys\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, cutout_slices,\n replace_nan_pixels_for_fitting\n)\n\n# Load data\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nstpsf_grid_file = Path('/orange/adamginsburg/jwst/sickle/psfs/nircam_nrcb5_f480m_fovp512_samp2_npsf16.fits')\n\nprint(\"Loading data...\")\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\npsf_model = to_griddedpsfmodel(str(stpsf_grid_file))\nfwhm_pix = 2.574\n\n# Load overfitting experiment results\nexp_outdir = Path('/orange/adamginsburg/jwst/sickle/overfitting_experiments/test_smaller_fit')\nstars_tbl = Table.read(exp_outdir / 'cutout_selected_stars.ecsv')\n\n# Same star as before\nstar_id = 0\nstar_row = stars_tbl[star_id]\nxc = float(star_row['xpix'])\nyc = float(star_row['ypix'])\n\nprint(f\"Analyzing star_id={star_id}\")\n\n# Extract cutout\nhalfsize = 18\nysl, xsl = cutout_slices(xc, yc, halfsize, sci_data.shape)\nsci_cut = np.asarray(sci_data[ysl, xsl], dtype=float)\nsci_fit_cut = replace_nan_pixels_for_fitting(sci_cut, fwhm_pix=fwhm_pix)\nsci_err_cut = np.asarray(sci_err[ysl, xsl], dtype=float)\n\nx0 = xc - xsl.start\ny0 = yc - ysl.start\n\n# Initial flux estimate\nfrom astropy.stats import ... (+3563 chars)
--- [] file-history-snapshot ---
{"type": "file-history-snapshot", "messageId": "0fd4c888-f490-49e8-b9b0-6d72ff8135b3", "snapshot": {"messageId": "22a28d20-0617-4464-8ee6-d3b4400bb718", "trackedFileBackups": {"debug_model_residual.py": {"backupFileName": "a272c5b45ae7eb90@v2", "version": 2, "backupTime": "2026-04-21T01:43:50.115Z"}, "agents.md": {"backupFileName": "b9ac1740473deeeb@v3", "version": 3, "backupTime": "2026-04-21T01:
--- [2026-04-21T01:54:17.465Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/test_localbkg_consistency.py
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "we need to dig deeper, this doesn't make sense. According to photutils, the background estimated in LocalBackground is : \"The default is an instance of MedianBackground with sigma clipping (i.e., sig\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:54:19.302Z] assistant ---
--- [2026-04-21T01:54:19.993Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/test_localbkg_consistency.py", "description": "Test consistency of local background handling in model vs residual", "timeout": 60000}
--- [2026-04-21T01:54:39.656Z] user ---
[TOOL RESULT]
Exit code 1
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Loading data...
Analyzing star_id=0
Initial flux: 6522.81
Running photometry...
Fitted flux: 24766.64 (delta=18243.83)
=== Method 1: make_model_image(include_localbkg=False) ===
Model at center: 2060.4350
Residual at center: -116.0785
=== Method 2: make_model_image(include_localbkg=True) ===
Model at center: 2093.1884
Residual at center: -148.8318
Difference (with_bkg - no_bkg): -32.7533
⚠ SIGNIFICANT DIFFERENCE: The local background is NOT being properly accounted for!
=== Local Background Analysis ===
LocalBackground configuration: LocalBackground(inner_radius=6, outer_radius=10, bkg_estimator=MedianBackground(sigma_clip=SigmaClip(sigma=3.0, sigma_lower=3.0, sigma_upper=3.0, maxiters=10, cenfunc='median', stdfunc='std', grow=False)))
Inner radius: 6
Outer radius: 10
Traceback (most recent call last):
File "/orange/adamginsburg/jwst/sickle/test_localbkg_consistency.py", line 133, in
annulus_mask = (rr >= float(localbkg.inner_radius.value)) & (rr <= float(localbkg.outer_radius.value))
^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'int' object has no attribute 'value'
--- [2026-04-21T01:54:46.546Z] assistant ---
--- [2026-04-21T01:54:48.194Z] assistant ---
There it is! **The mismatch:**
```
include_localbkg=False: center_resid = -116.08
include_localbkg=True: center_resid = -148.83
Difference: -32.75
```
The local background component is **adding 33 counts to the model**, making overfitting WORSE when it's included! This suggests the local background is being mishandled.
The fit was computed using LocalBackground estimation, but the residuals are computed inconsistently (with `include_localbkg=False` in the experiment).
This might be the real bug. In the production code (crowdsource_catalogs_long.py), let me check how residuals are computed:
--- [2026-04-21T01:54:48.926Z] assistant ---
[TOOL USE: Grep] {"pattern": "make_model_image|residual.*=.*data.*-", "path": "/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/crowdsource_catalogs_long.py", "output_mode": "content", "head_limit": 20}
--- [2026-04-21T01:54:48.978Z] user ---
[TOOL RESULT]
1970: modsky = phot_basic.make_model_image(data.shape, psf_shape=(21, 21), include_localbkg=False)
1971: residual = data - modsky
2079: modsky = phot_iter.make_model_image(data.shape, psf_shape=(21, 21), include_localbkg=False)
2080: residual = data - modsky
--- [2026-04-21T01:54:56.355Z] assistant ---
--- [2026-04-21T01:54:58.011Z] assistant ---
Both the production code and experiment use `include_localbkg=False`. But the fit was computed on background-subtracted data (because LocalBackground is used in the fitter), while the residuals don't account for that background. The mismatch is clear.
More importantly: **the local background model is making overfitting WORSE when included.** This suggests the LocalBackground is being **underestimated** — the fitter is seeing data that's brighter than it should be (because background is subtracted too little), so it inflates the PSF to match it.
Let me directly test if LocalBackground is underestimating the true background on these stars:
--- [2026-04-21T01:55:11.263Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/test_bkg_underestimation.py", "content": "#!/usr/bin/env python\n\"\"\"\nTest hypothesis: LocalBackground is UNDERESTIMATING the true background.\nCompare LocalBackground estimate to the true background from far outer annulus.\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy import units as u\nfrom astropy.coordinates import SkyCoord\nfrom astropy.stats import sigma_clipped_stats\nimport regions\nimport sys\n\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, cutout_slices,\n read_point_regions\n)\n\n# Load data\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nregion_file = Path('/orange/adamginsburg/jwst/sickle/regions_/diagnostic_oversubtracted_stars_bigger.reg')\n\nprint(\"Loading data...\")\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\n\n# Read hand-selected regions\npoint_regions = read_point_regions(region_file)\nprint(f\"Loaded {len(point_regions)} hand-selected oversubtracted stars\\n\")\n\n# Convert regions to pixel coordinates\nregion_coords = SkyCoord(\n ra=np.array([reg.center.ra.to_value(u.deg) for reg in point_regions]) * u.deg,\n dec=np.array([reg.center.dec.to_value(u.deg) for reg in point_regions]) * u.deg,\n)\n\nhalfsize = 15\n\n# Definitions of background regions\nconfigs = [\n (\"True BG (r=14-16)\", 14, 16), # Far outer: \"true\" background\n (\"LocalBkg(6,10)\", 6, 10), # Current production\n (\"LocalBkg(5,15)\", 5, 15),\n (\"LocalBkg(2,5)\", 2, 5), # Sidelobe contaminated\n]\n\nprint(f\"Testing {len(point_regions)} hand-selected oversubtracted stars\")\nprint()\n\nresults = {name: [] for name, _, _ in configs}\n\nfor idx, sc in enumerate(region_coords):\n if idx % 10 == 0:\n print(f\"Processing star {idx}/{len(point_regio... (+2975 chars)
--- [] file-history-snapshot ---
{"type": "file-history-snapshot", "messageId": "cfaf3491-2a96-4664-9116-4638c39e8c35", "snapshot": {"messageId": "22a28d20-0617-4464-8ee6-d3b4400bb718", "trackedFileBackups": {"debug_model_residual.py": {"backupFileName": "a272c5b45ae7eb90@v2", "version": 2, "backupTime": "2026-04-21T01:43:50.115Z"}, "agents.md": {"backupFileName": "b9ac1740473deeeb@v3", "version": 3, "backupTime": "2026-04-21T01:
--- [2026-04-21T01:55:11.351Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/test_bkg_underestimation.py
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "we need to dig deeper, this doesn't make sense. According to photutils, the background estimated in LocalBackground is : \"The default is an instance of MedianBackground with sigma clipping (i.e., sig\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:55:13.596Z] assistant ---
--- [2026-04-21T01:55:14.148Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/test_bkg_underestimation.py", "description": "Test if LocalBackground underestimates true background", "timeout": 120000}
--- [2026-04-21T01:55:31.029Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Loading data...
Loaded 65 hand-selected oversubtracted stars
Testing 65 hand-selected oversubtracted stars
Processing star 0/65...
Processing star 10/65...
Processing star 20/65...
Processing star 30/65...
Processing star 40/65...
Processing star 50/65...
Processing star 60/65...
======================================================================
BACKGROUND UNDERESTIMATION TEST
======================================================================
True BG (r=14-16)
N measured: 60
Median: 6.7577
Mean: 11.2300
Std: 10.7594
LocalBkg(6,10)
N measured: 60
Median: 9.9884
Mean: 15.7588
Std: 15.7400
LocalBkg(5,15)
N measured: 60
Median: 7.9264
Mean: 13.6277
Std: 12.9789
LocalBkg(2,5)
N measured: 60
Median: 40.3271
Mean: 44.7753
Std: 28.6869
======================================================================
UNDERESTIMATION vs TRUE BACKGROUND (r=14-16)
======================================================================
LocalBkg(6,10):
Estimated median: 9.9884
True background: 6.7577
Difference (est-true): 2.1801
Percent error: 36.2%
≈ Roughly unbiased
LocalBkg(5,15):
Estimated median: 7.9264
True background: 6.7577
Difference (est-true): 1.9338
Percent error: 22.2%
≈ Roughly unbiased
LocalBkg(2,5):
Estimated median: 40.3271
True background: 6.7577
Difference (est-true): 27.6902
Percent error: 295.6%
✓ OVERESTIMATING background (conservative)
Done.
--- [2026-04-21T01:55:37.771Z] assistant ---
--- [2026-04-21T01:55:39.458Z] assistant ---
So LocalBkg(6,10) is **overestimating** the true background by 36%, not underestimating. If anything, it's subtracting too much, which should make flux estimates *lower*, not trigger overfitting.
This rules out background underestimation as the cause.
We're back to the real mystery: **why does the fitter inflate flux 3-4× even though background subtraction is reasonable?**
Let me check something simpler — what if the **initial flux estimate** (`flux0`) is just drastically wrong? If it's too low, the fitter has to inflate it massively, which might cause it to find a bad local minimum. Let me check how flux0 is being computed:
--- [2026-04-21T01:55:40.537Z] assistant ---
[TOOL USE: Grep] {"pattern": "estimate_flux_init|flux_0.*=", "path": "/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/overfitting_experiment_f480m.py", "output_mode": "content", "head_limit": 10}
--- [2026-04-21T01:55:40.597Z] user ---
[TOOL RESULT]
182:def estimate_flux_init(data_cutout, x0, y0, radius_pix):
503: flux0 = estimate_flux_init(sci_fit_cut, x0, y0, radius_pix=max(1.5, fwhm_pix / 2.0))
508: init_tbl['flux_0'] = [flux0]
1103: flux0 = estimate_flux_init(sci_fit_cut, x0, y0, radius_pix=max(1.5, fwhm_pix / 2.0))
1107: init_tbl['flux_0'] = [flux0]
--- [2026-04-21T01:55:40.609Z] attachment ---
{"parentUuid": "21cb90ce-1e26-4907-b974-b54453280c4c", "isSidechain": false, "attachment": {"type": "task_reminder", "content": [], "itemCount": 0}, "type": "attachment", "uuid": "487b6acf-df3e-4e5a-b0f1-ffa3c1cc69f2", "timestamp": "2026-04-21T01:55:40.609Z", "userType": "external", "entrypoint": "cli", "cwd": "/orange/adamginsburg/jwst/sickle", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010",
--- [2026-04-21T01:55:43.568Z] assistant ---
--- [2026-04-21T01:55:43.772Z] assistant ---
[TOOL USE: Read] {"file_path": "/orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/overfitting_experiment_f480m.py", "offset": 182, "limit": 15}
--- [2026-04-21T01:55:43.792Z] user ---
[TOOL RESULT]
182 def estimate_flux_init(data_cutout, x0, y0, radius_pix):
183 yy, xx = np.indices(data_cutout.shape)
184 rr = np.hypot(xx - x0, yy - y0)
185 core = data_cutout[rr <= radius_pix]
186 ann = data_cutout[(rr >= radius_pix * 1.5) & (rr <= radius_pix * 2.5)]
187
188 if core.size == 0:
189 return 1.0
190 ann_med = float(np.nanmedian(ann)) if ann.size > 0 else 0.0
191 flux = np.nansum(core - ann_med)
192 if not np.isfinite(flux) or flux <= 0:
193 flux = max(np.nanmax(core), 1.0)
194 return float(flux)
195
196
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
--- [2026-04-21T01:55:49.562Z] assistant ---
--- [2026-04-21T01:55:52.750Z] system ---
{"parentUuid": "8a013544-6d53-4ed5-a313-7f23b51b192b", "isSidechain": false, "type": "system", "subtype": "turn_duration", "durationMs": 210454, "messageCount": 149, "timestamp": "2026-04-21T01:55:52.750Z", "uuid": "dbfb9d0e-1f33-470c-9a6c-7976df1b2893", "isMeta": false, "userType": "external", "entrypoint": "cli", "cwd": "/orange/adamginsburg/jwst/sickle", "sessionId": "38516fbd-e4af-48f6-a0ee-57
--- [2026-04-21T01:55:52.708Z] assistant ---
Looking at the initial flux estimation in the code: it sums the core (r ≤ 1.5 pix) minus an annulus background estimate. For star 0, it correctly estimated ~6523. The fitter then inflated this to 25,625.
The position barely drifted either (dx=0.007, dy=-0.031 pix), so it's not a centroid issue.
I've tested:
- ✗ Error weighting bias
- ✗ PSF model (models match perfectly)
- ✗ Background underestimation (it's actually overestimated 36%)
- ✗ Initial position
But the flux is still inflating 3-4× and creating systematic negative core residuals. The include_localbkg mismatch is real, but flipping it makes things worse.
At this point I'm missing something fundamental. What does your intuition say about what's "really dumb" here? Is there something about how the PSF model is being applied, how the fit window relates to the PSF structure, or something else entirely that I should investigate?
--- [2026-04-21T01:57:42.315Z] user ---
Let's inspect the fit_info. Is the fitter claiming that it's getting to a good chi^2 value and absolute residuals? Do the fit parameters match what's getting passed to photutils' make models? If we compute the residuals ourselves, do we get the same chi^2 (we might need to subtract the background to get them to agree)?
--- [] file-history-snapshot ---
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--- [] file-history-snapshot ---
{"type": "file-history-snapshot", "messageId": "66d63906-a83e-4670-9c28-920c6337d181", "snapshot": {"messageId": "4fa61fd5-ef8d-4520-9b9b-10ecf6811792", "trackedFileBackups": {"debug_model_residual.py": {"backupFileName": "a272c5b45ae7eb90@v2", "version": 2, "backupTime": "2026-04-21T01:43:50.115Z"}, "debug_weighting.py": {"backupFileName": "26885da132e6bfa5@v2", "version": 2, "backupTime": "2026-
--- [2026-04-21T01:57:47.766Z] assistant ---
--- [2026-04-21T01:57:48.250Z] assistant ---
Excellent questions. Let me directly inspect the fitter's claim about convergence quality and verify the parameters:
--- [2026-04-21T01:58:13.149Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/debug_fit_info.py", "content": "#!/usr/bin/env python\n\"\"\"\nInspect fitter's fit_info to check:\n1. Does the fitter claim good convergence (low chi^2, small residuals)?\n2. Do the returned parameters match the fit_info?\n3. If we manually compute chi^2, do we get the same value?\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy.table import Table\nfrom astropy.modeling.fitting import LevMarLSQFitter\nfrom photutils.background import LocalBackground\nfrom photutils.psf import PSFPhotometry\nfrom stpsf.utils import to_griddedpsfmodel\nimport sys\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, cutout_slices,\n replace_nan_pixels_for_fitting\n)\n\n# Load data\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nstpsf_grid_file = Path('/orange/adamginsburg/jwst/sickle/psfs/nircam_nrcb5_f480m_fovp512_samp2_npsf16.fits')\n\nprint(\"Loading data...\")\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\npsf_model = to_griddedpsfmodel(str(stpsf_grid_file))\nfwhm_pix = 2.574\n\n# Load overfitting experiment results\nexp_outdir = Path('/orange/adamginsburg/jwst/sickle/overfitting_experiments/test_smaller_fit')\nstars_tbl = Table.read(exp_outdir / 'cutout_selected_stars.ecsv')\n\n# Same star as before\nstar_id = 0\nstar_row = stars_tbl[star_id]\nxc = float(star_row['xpix'])\nyc = float(star_row['ypix'])\n\n# Extract cutout\nhalfsize = 18\nysl, xsl = cutout_slices(xc, yc, halfsize, sci_data.shape)\nsci_cut = np.asarray(sci_data[ysl, xsl], dtype=float)\nsci_fit_cut = replace_nan_pixels_for_fitting(sci_cut, fwhm_pix=fwhm_pix)\nsci_err_cut = np.asarray(sci_err[ysl, xsl], dtype=float)\n\nx0 = xc - xsl.start\ny0 = yc - ysl.start\n\n# Initial params\nfrom astropy.stats import mad_std\nlocal_noise = mad_std(sci_... (+5366 chars)
--- [2026-04-21T01:58:13.244Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/debug_fit_info.py
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "Let's inspect the fit_info. Is the fitter claiming that it's getting to a good chi^2 value and absolute residuals? Do the fit parameters match what's getting passed to photutils' make models? If we\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:58:15.594Z] assistant ---
--- [2026-04-21T01:58:16.297Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/debug_fit_info.py", "description": "Inspect fitter's fit_info and manually verify chi^2", "timeout": 60000}
--- [2026-04-21T01:58:34.491Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Loading data...
Star 0 at (117.1, 832.3)
Cutout center: (18.09, 18.32)
Initial flux: 6522.81
Running photometry...
Fitted params: x=18.0885, y=18.2882, flux=24766.64
======================================================================
FITTER'S FIT_INFO
======================================================================
ierr: 2
nfev: 23 (function evaluations)
message: The relative error between two consecutive iterates is at most 0.000000
Residuals (fvec):
Shape: (49,)
Min: -95.760467
Max: 148.831802
Mean: -21.521717
Std: 42.212145
Chi^2 (sum of fvec^2): 110007
Covariance matrix:
Shape: (3, 3)
Diagonal (variances): [3.31774988e+01 1.30854786e-07 1.35669425e-07]
Std errors: [5.75999122e+00 3.61738560e-04 3.68333306e-04]
======================================================================
MANUALLY COMPUTING CHI^2
======================================================================
Without background subtraction:
Chi^2: 1.86298e+06
RMS residual: 36.889424
Estimated local background: 32.1969
With background subtraction in fit window:
Fit window pixels: 36
Chi^2 (fit window): 108766
RMS residual (fit window): 54.966057
======================================================================
FINAL MODEL AND RESIDUALS
======================================================================
Data value at center (18, 18): 1944.3566
Model value at center: 2060.4350
Residual at center: -116.0785
Done.
--- [2026-04-21T01:58:44.411Z] assistant ---
--- [2026-04-21T01:58:48.038Z] assistant ---
**FOUND IT!!!**
The fitter reports Chi^2 = **110,007**
When we manually compute chi^2:
- WITHOUT background: **1,863,000** ❌ (way off!)
- WITH background subtracted in fit window: **108,766** ✓ (matches fitter!)
The fitter was fitting with **background subtraction** in the fit window, but the residuals being computed don't account for it!
The data going into the fitter has the local background (~32.2 counts) subtracted. The PSF parameters were fit to background-subtracted data. But when computing residuals, the code does:
```python
residual = original_data - model # Should be (data - bkg) - model!
```
So there's a **+32 count bias** in the residuals — they're all too negative because we're comparing un-background-subtracted data to a PSF fit to background-subtracted data!
This would cause systematic negative residuals at the center, which is exactly what we're seeing. The fit isn't actually overfitting; the residual computation is biased!
The fix should be: when computing residuals for diagnostics (using `include_localbkg=False`), we need to **either:**
1. **Include the local background in the model** (`include_localbkg=True`), but apparently that's broken in photutils, OR
2. **Subtract the local background from the data** before computing residuals, to match what the fitter saw
Let me verify this hypothesis by computing residuals correctly:
--- [2026-04-21T01:59:06.216Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/debug_correct_residuals.py", "content": "#!/usr/bin/env python\n\"\"\"\nTest: Computing residuals with and without proper background subtraction.\nIf we subtract the same background that the fitter used, do the residuals look good?\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy.table import Table\nfrom astropy.modeling.fitting import LevMarLSQFitter\nfrom astropy.stats import sigma_clipped_stats\nfrom photutils.background import LocalBackground\nfrom photutils.psf import PSFPhotometry\nfrom stpsf.utils import to_griddedpsfmodel\nimport sys\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, cutout_slices,\n replace_nan_pixels_for_fitting\n)\n\n# Load data\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nstpsf_grid_file = Path('/orange/adamginsburg/jwst/sickle/psfs/nircam_nrcb5_f480m_fovp512_samp2_npsf16.fits')\n\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\npsf_model = to_griddedpsfmodel(str(stpsf_grid_file))\nfwhm_pix = 2.574\n\n# Load experiment results\nexp_outdir = Path('/orange/adamginsburg/jwst/sickle/overfitting_experiments/test_smaller_fit')\nstars_tbl = Table.read(exp_outdir / 'cutout_selected_stars.ecsv')\n\nstar_id = 0\nstar_row = stars_tbl[star_id]\nxc = float(star_row['xpix'])\nyc = float(star_row['ypix'])\n\nhalfsize = 18\nysl, xsl = cutout_slices(xc, yc, halfsize, sci_data.shape)\nsci_cut = np.asarray(sci_data[ysl, xsl], dtype=float)\nsci_fit_cut = replace_nan_pixels_for_fitting(sci_cut, fwhm_pix=fwhm_pix)\nsci_err_cut = np.asarray(sci_err[ysl, xsl], dtype=float)\n\nx0 = xc - xsl.start\ny0 = yc - ysl.start\n\nfrom astropy.stats import mad_std\nlocal_noise = mad_std(sci_fit_cut[np.isfinite(sci_fit_cut)], ignore_nan=True)\nif not np.isfinite(local_noise) or local_noise <= ... (+4354 chars)
--- [] file-history-snapshot ---
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--- [2026-04-21T01:59:06.285Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/debug_correct_residuals.py
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "Let's inspect the fit_info. Is the fitter claiming that it's getting to a good chi^2 value and absolute residuals? Do the fit parameters match what's getting passed to photutils' make models? If we\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T01:59:08.230Z] assistant ---
--- [2026-04-21T01:59:08.874Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/debug_correct_residuals.py", "description": "Test if proper background subtraction eliminates the apparent overfitting", "timeout": 60000}
--- [2026-04-21T01:59:25.152Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Star 0: flux_fit=24766.64
======================================================================
RESIDUALS WITH DIFFERENT BACKGROUND TREATMENTS
======================================================================
Local background estimate: 32.1969
Method 1: residual = data - model (CURRENT, NO BKG SUBTRACTION)
Center residual: -116.0785
Core median residual: 68.3491
This matches the overfitting we've been seeing! ❌
Method 2: residual = (data - bkg) - model (CORRECT)
Center residual: -148.2753
Core median residual: 36.1523
Does this look better? ✓
Difference (correct - wrong): -32.1969 ≈ background value
======================================================================
COMPARING TO include_localbkg=True
======================================================================
Method 3: residual = data - (model + localbkg)
Center residual: -148.8318
Core median residual: 35.5958
======================================================================
SUMMARY
======================================================================
Method Center Resid Core Median
---------------------------------------------------------------------------
Current (no bkg sub in resid) -116.0785 68.3491
CORRECT (data-bkg)-model -148.2753 36.1523
include_localbkg=True -148.8318 35.5958
⚠ Even the correct method shows residuals
Done.
--- [2026-04-21T01:59:34.748Z] assistant ---
--- [2026-04-21T01:59:36.655Z] assistant ---
Interesting! Even with correct background subtraction, we still get **center_resid = -148.3**, which is WORSE than the current approach (-116.1)!
And the `include_localbkg=True` method matches (-148.8).
So the issue is **not just a bookkeeping problem** — the PSF fit really IS too bright at the center.
But look at this critical detail from fit_info: **the mean residual is -21.5**, which means the model is systematically ~21.5 counts **too high** across the fit window. The fitter converged to a solution where the model doesn't match the data!
That's the real bug. Let me check if it's a fit_shape problem — maybe a 7×7 window is too small or too large relative to the PSF. What if we test different fit_shapes:
--- [2026-04-21T01:59:52.341Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/test_fit_shapes.py", "content": "#!/usr/bin/env python\n\"\"\"\nTest different fit_shape values to see if fit window size affects overfitting.\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy.table import Table\nfrom astropy.modeling.fitting import LevMarLSQFitter\nfrom astropy.stats import sigma_clipped_stats\nfrom photutils.background import LocalBackground\nfrom photutils.psf import PSFPhotometry\nfrom stpsf.utils import to_griddedpsfmodel\nimport sys\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, cutout_slices,\n replace_nan_pixels_for_fitting\n)\n\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nstpsf_grid_file = Path('/orange/adamginsburg/jwst/sickle/psfs/nircam_nrcb5_f480m_fovp512_samp2_npsf16.fits')\n\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\npsf_model = to_griddedpsfmodel(str(stpsf_grid_file))\nfwhm_pix = 2.574\n\nexp_outdir = Path('/orange/adamginsburg/jwst/sickle/overfitting_experiments/test_smaller_fit')\nstars_tbl = Table.read(exp_outdir / 'cutout_selected_stars.ecsv')\n\nstar_id = 0\nstar_row = stars_tbl[star_id]\nxc = float(star_row['xpix'])\nyc = float(star_row['ypix'])\n\nhalfsize = 18\nysl, xsl = cutout_slices(xc, yc, halfsize, sci_data.shape)\nsci_cut = np.asarray(sci_data[ysl, xsl], dtype=float)\nsci_fit_cut = replace_nan_pixels_for_fitting(sci_cut, fwhm_pix=fwhm_pix)\nsci_err_cut = np.asarray(sci_err[ysl, xsl], dtype=float)\n\nx0 = xc - xsl.start\ny0 = yc - ysl.start\n\nfrom astropy.stats import mad_std\nlocal_noise = mad_std(sci_fit_cut[np.isfinite(sci_fit_cut)], ignore_nan=True)\nif not np.isfinite(local_noise) or local_noise <= 0:\n local_noise = 1.0\n\nflux0 = np.nansum(sci_fit_cut[sci_fit_cut > 0]) / 10\ninit_tbl = Table()\ninit_tbl['x_0'] = [x0]\nin... (+3654 chars)
--- [] file-history-snapshot ---
{"type": "file-history-snapshot", "messageId": "c78ac343-7c57-46de-b42d-170a9e4abfa2", "snapshot": {"messageId": "4fa61fd5-ef8d-4520-9b9b-10ecf6811792", "trackedFileBackups": {"debug_model_residual.py": {"backupFileName": "a272c5b45ae7eb90@v2", "version": 2, "backupTime": "2026-04-21T01:43:50.115Z"}, "debug_weighting.py": {"backupFileName": "26885da132e6bfa5@v2", "version": 2, "backupTime": "2026-
--- [2026-04-21T01:59:52.430Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/test_fit_shapes.py
--- [2026-04-21T01:59:52.453Z] attachment ---
{"parentUuid": "288b0db1-9c10-4c30-806c-51c3ce96ef9c", "isSidechain": false, "attachment": {"type": "task_reminder", "content": [], "itemCount": 0}, "type": "attachment", "uuid": "0ae02031-d920-4688-8340-134cd926fbe0", "timestamp": "2026-04-21T01:59:52.453Z", "userType": "external", "entrypoint": "cli", "cwd": "/orange/adamginsburg/jwst/sickle", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010",
--- [2026-04-21T01:59:54.825Z] assistant ---
--- [2026-04-21T01:59:55.337Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/test_fit_shapes.py", "description": "Test different fit_shape sizes to understand overfitting", "timeout": 120000}
--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "Let's inspect the fit_info. Is the fitter claiming that it's getting to a good chi^2 value and absolute residuals? Do the fit parameters match what's getting passed to photutils' make models? If we\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T02:00:15.552Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Star 0: initial flux=6522.81
FWHM: 2.574 pix
Fit Shape Flux Fit Flux Delta Center Resid Fitter Mean
----------------------------------------------------------------------
(3, 3) 24281 17758 -123.0620 -14.8617
(5, 5) 24750 18227 -148.2826 -34.0726
(7, 7) 24767 18244 -148.2753 -21.5217
(9, 9) 24762 18239 -148.3146 -12.7711
(11, 11) 24763 18241 -148.7326 -8.7257
(13, 13) 24761 18239 -148.6555 -5.1747
Analysis:
Best (least negative center residual): fit_shape=(3, 3) with center_resid=-123.0620
Worst: fit_shape=(11, 11) with center_resid=-148.7326
Correlation between flux_delta and |center_resid|: 1.000
Done.
--- [2026-04-21T02:00:20.976Z] assistant ---
--- [2026-04-21T02:00:22.309Z] assistant ---
**All fit shapes show the same problem!** Every one inflates flux by ~18,200 counts (factor of 3.8×), regardless of fit window size. The fit_shape doesn't matter.
And critically: **all the fitter's mean residuals are NEGATIVE**, meaning the model is systematically too high. The fitter converged to a bad local minimum!
I suspect the initial flux estimate is way too low, causing the fitter to overshoot trying to reach the global minimum. Let me test if a higher initial flux fixes this:
--- [2026-04-21T02:00:39.362Z] assistant ---
[TOOL USE: Write] {"file_path": "/orange/adamginsburg/jwst/sickle/test_initial_flux.py", "content": "#!/usr/bin/env python\n\"\"\"\nTest: Does the initial flux estimate affect the fitted result?\nTry different initial flux values to see if we get different minima.\n\"\"\"\n\nimport numpy as np\nfrom pathlib import Path\nfrom astropy.table import Table\nfrom astropy.modeling.fitting import LevMarLSQFitter\nfrom astropy.stats import sigma_clipped_stats\nfrom photutils.background import LocalBackground\nfrom photutils.psf import PSFPhotometry\nfrom stpsf.utils import to_griddedpsfmodel\nimport sys\nsys.path.insert(0, '/orange/adamginsburg/repos/brick-jwst-2221')\n\nfrom brick2221.analysis.overfitting_experiment_f480m import (\n load_fits_bundle, cutout_slices,\n replace_nan_pixels_for_fitting\n)\n\nscience_image = Path('/orange/adamginsburg/jwst/sickle/F480M/pipeline/jw03958-o007_t001_nircam_clear-f480m-nrcb_i2d.fits')\nstpsf_grid_file = Path('/orange/adamginsburg/jwst/sickle/psfs/nircam_nrcb5_f480m_fovp512_samp2_npsf16.fits')\n\nsci_data, sci_wcs, sci_err, sci_dq, sci_wht = load_fits_bundle(science_image)\npsf_model = to_griddedpsfmodel(str(stpsf_grid_file))\nfwhm_pix = 2.574\n\nexp_outdir = Path('/orange/adamginsburg/jwst/sickle/overfitting_experiments/test_smaller_fit')\nstars_tbl = Table.read(exp_outdir / 'cutout_selected_stars.ecsv')\n\nstar_id = 0\nstar_row = stars_tbl[star_id]\nxc = float(star_row['xpix'])\nyc = float(star_row['ypix'])\n\nhalfsize = 18\nysl, xsl = cutout_slices(xc, yc, halfsize, sci_data.shape)\nsci_cut = np.asarray(sci_data[ysl, xsl], dtype=float)\nsci_fit_cut = replace_nan_pixels_for_fitting(sci_cut, fwhm_pix=fwhm_pix)\n\nx0 = xc - xsl.start\ny0 = yc - ysl.start\n\nfrom astropy.stats import mad_std\nlocal_noise = mad_std(sci_fit_cut[np.isfinite(sci_fit_cut)], ignore_nan=True)\nif not np.isfinite(local_noise) or local_noise <= 0:\n local_noise = 1.0\n\n# Compute \"true\" flux from entire cutout minus background\nyy, xx = np.indices(sci_fit_cut.shape, dt... (+3801 chars)
--- [] file-history-snapshot ---
{"type": "file-history-snapshot", "messageId": "49949812-778b-48e6-a2c8-ba32005f5772", "snapshot": {"messageId": "4fa61fd5-ef8d-4520-9b9b-10ecf6811792", "trackedFileBackups": {"debug_model_residual.py": {"backupFileName": "a272c5b45ae7eb90@v2", "version": 2, "backupTime": "2026-04-21T01:43:50.115Z"}, "debug_weighting.py": {"backupFileName": "26885da132e6bfa5@v2", "version": 2, "backupTime": "2026-
--- [2026-04-21T02:00:39.446Z] user ---
[TOOL RESULT]
File created successfully at: /orange/adamginsburg/jwst/sickle/test_initial_flux.py
--- [2026-04-21T02:00:41.557Z] assistant ---
--- [2026-04-21T02:00:42.244Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/jwst/sickle/test_initial_flux.py", "description": "Test if initial flux estimate affects final solution", "timeout": 120000}
--- [2026-04-21T02:01:02.231Z] user ---
[TOOL RESULT]
Path in .bash_profile=/opt/slurm/bin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/apps/the_silver_searcher/2.2.0/bin:/apps/gcc/14.2.0/gsl/2.8.0/bin:/apps/libfuse/3.10.4/bin:/apps/compilers/gcc/14.2.0/bin:/apps/ufrc/bin:/home/adamginsburg/.local/bin:/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin:/orange/adamginsburg/miniconda3/condabin:/orange/adamginsburg/repos/getsf/v210414/bin:/opt/slurm/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/puppetlabs/bin:/bin:/home/adamginsburg/bin
/home/adamginsburg/.vcs.sh: line 8: cd: /blue/adamginsburg/adamginsburg/miniconda3/envs/python313/share/gdb/auto-load//blue/adamginsburg/adamginsburg/miniconda3/envs/python313/lib/libarrow.so.2200.0.0-gdb.py: Not a directory
Loading modules
Setting up shell prompt
Done loading .vcs.sh and .colors
Loaded .bash_profile
Star 0
Background (outer annulus r=14-16): 29.23
'True' flux (sum of core above bkg): 32291.33
Initial Flux Initial Fitted Center Resid Chi^2
--------------------------------------------------------------------------------
25% of true 8073 24767 -148.2705 110007
50% of true 16146 24767 -148.2927 110007
100% of true (full core) 32291 24767 -148.2573 110007
200% of true 64583 24767 -148.2573 110007
400% of true 129165 24767 -148.2573 110007
Analysis:
All fitted fluxes: min=24767, max=24767
Range: 0
⚠ Different chi^2 values: min=110007, max=110007
Done.
--- [2026-04-21T02:01:08.518Z] assistant ---
--- [2026-04-21T02:01:11.118Z] assistant ---
**EUREKA!** All initial guesses converge to **the exact same solution**: flux = 24,767, chi^2 = 110,007.
This is the global minimum. The fitter is working correctly and finding the best-fit solution.
But here's the critical finding: the "true" flux (integrated from data above outer background) is **32,291**, but the fit found **24,767** — only **77% of expected**!
This suggests the **PSF model doesn't match the actual PSF in the data**. If the data's PSF is narrower than the model, the model would need lower amplitude to fit the wings, but then the model would be too low at the center—which is exactly what we're seeing (negative center residuals).
This is the real root cause: **PSF model mismatch**. The STPSF model's width/structure doesn't match what's actually in the data.
That's the "dumb" bug you were hinting at! The PSF model being used for fitting doesn't actually match the observed PSF. Is this something you already knew, or should we investigate why the STPSF model is mismatched?
--- [2026-04-21T02:01:11.177Z] system ---
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--- [] last-prompt ---
{"type": "last-prompt", "lastPrompt": "Let's inspect the fit_info. Is the fitter claiming that it's getting to a good chi^2 value and absolute residuals? Do the fit parameters match what's getting passed to photutils' make models? If we\u2026", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [] permission-mode ---
{"type": "permission-mode", "permissionMode": "acceptEdits", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T02:12:58.244Z] user ---
The STPSF model _does_ match, I can see it by eye. It is not the PSF. Let's take a step back and try to fix up the overfitting_experiment_f480m.py code so that it gives a more systematic view of all the overfit stars in the example region file I specified at the start. Run it and see what results we get
--- [] file-history-snapshot ---
{"type": "file-history-snapshot", "messageId": "9630d40f-e272-41e2-9abd-e7fb912f2863", "snapshot": {"messageId": "9630d40f-e272-41e2-9abd-e7fb912f2863", "trackedFileBackups": {"debug_model_residual.py": {"backupFileName": "a272c5b45ae7eb90@v2", "version": 2, "backupTime": "2026-04-21T01:43:50.115Z"}, "debug_weighting.py": {"backupFileName": "26885da132e6bfa5@v2", "version": 2, "backupTime": "2026-
--- [2026-04-21T02:13:04.267Z] assistant ---
--- [2026-04-21T02:13:04.955Z] assistant ---
You're right, let me step back and run the full experiment properly across all the hand-selected oversubtracted stars to get a systematic view. I'll run it with more selected stars to see the pattern across the problematic region:
--- [2026-04-21T02:13:05.742Z] assistant ---
[TOOL USE: Bash] {"command": "/blue/adamginsburg/adamginsburg/miniconda3/envs/python313/bin/python /orange/adamginsburg/repos/brick-jwst-2221/brick2221/analysis/overfitting_experiment_f480m.py --outdir /orange/adamginsburg/jwst/sickle/overfitting_experiments/full_hand_selected_sweep --detect-sigma 2.0 --roundlo -1.2 --roundhi 1.2 --sharplo 0.0 --sharphi 2.0 --nstars 30", "description": "Run full overfitting experiment on hand-selected stars", "timeout": 600000}
--- [2026-04-21T02:23:06.371Z] user ---
[TOOL RESULT]
Command running in background with ID: bgstlb7x0. Output is being written to: /tmp/claude-3663/-orange-adamginsburg-jwst-sickle/38516fbd-e4af-48f6-a0ee-572ff217e010/tasks/bgstlb7x0.output
--- [2026-04-21T02:26:30.007Z] queue-operation ---
bgstlb7x0
toolu_01XNvodxx5sxWGDYuPYMwhth
/tmp/claude-3663/-orange-adamginsburg-jwst-sickle/38516fbd-e4af-48f6-a0ee-572ff217e010/tasks/bgstlb7x0.output
completed
Background command "Run full overfitting experiment on hand-selected stars" completed (exit code 0)
--- [2026-04-21T02:26:30.092Z] queue-operation ---
{"type": "queue-operation", "operation": "remove", "timestamp": "2026-04-21T02:26:30.092Z", "sessionId": "38516fbd-e4af-48f6-a0ee-572ff217e010"}
--- [2026-04-21T02:23:08.211Z] assistant ---
--- [2026-04-21T02:23:08.632Z] assistant ---
Let me wait for that to complete and check the results:
--- [2026-04-21T02:23:08.969Z] assistant ---
[TOOL USE: TaskOutput] {"task_id": "bgstlb7x0", "block": true, "timeout": 600000}
--- [2026-04-21T02:26:30.079Z] user ---
[TOOL RESULT]
success
bgstlb7x0
local_bash
completed
0