<scp>astrophot</scp>: fitting everything everywhere all at once in astronomical images
Bibliographic record
Abstract
ABSTRACT We present astrophot, a fast, powerful, and user-friendly python based astronomical image photometry solver. astrophot incorporates automatic differentiation and graphics processing unit (GPU), or parallel central processing unit (CPU), acceleration, powered by the machine learning library pytorch. Everything: astrophot can fit models for sky, stars, galaxies, point spread functions (PSFs), and more in a principled χ2 forward optimization, recovering Bayesian posterior information and covariance of all parameters. Everywhere: astrophot can optimize forward models on CPU or GPU; across images that are large, multiband, multi-epoch, rotated, dithered, and more. All at once: The models are optimized together, thus handling overlapping objects and including the covariance between parameters (including PSF and galaxy parameters). A number of optimization algorithms are available including Levenberg–Marquardt, Gradient descent, and No-U-Turn Markov chain Monte Carlo sampling. With an object-oriented user interface, astrophot makes it easy to quickly extract detailed information from complex astronomical data for individual images or large survey programs. This paper outlines novel features of the astrophot code and compares it to other popular astronomical image modelling software. astrophot is open-source, fully python based, and freely accessible at https://github.com/Autostronomy/AstroPhot .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.034 |
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".