A Comparative Analysis of 2-Dimensional Model Fitting Algorithms for Astronomical Images
Bibliographic record
Abstract
Stars and galaxies are captured from light-years away by telescopes and other observational instruments, which produce a pixel grid containing values indicating the light emitted by the object. These pixels have no meaning on their own; important information on the observed object is obtained by fitting models. AstroPhot is a Python-based photometry tool built for fitting such models to astronomical images and uses chi-squared 2D forward modeling to analyze the information they contain. AstroPhot performs sub-pixel integration to ensure high accuracy. In common galaxy models such as Sérsic, brightness within pixels may vary rapidly, so sub-pixel integration is essential to precisely portray these models. Efficiently and accurately performing this type of integration is challenging and many techniques exist to solve this problem. We seek to determine the optimal algorithms/parameters to ensure speed and reliability. To probe this question, AstroPhot galaxy models were compared to those generated by GALFIT, an established photometry solver that also uses chi-squared minimization for fitting. Ideally AstroPhot and GALFIT represent the same model, but due to their differing sub-pixel integration methods, there will be subtle variations in their values. Comparing models from the two algorithms required careful unit conversions due to their different surface brightness parameters. Using a high-resolution image generated with GALFIT as a reference, we sought to determine which pixels in AstroPhot models need more integration, as well as how to get the most accurate pixel value most efficiently.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".