dfreproject: A Python package for astronomical reprojection
Bibliographic record
Abstract
Deep astronomical images are often constructed by digitally stacking many individual subexposures.Each sub-exposure is expected to show small differences in the positions of stars and other objects in the field, due to the movement of the celestial bodies, changes/imperfections in the opto-mechanical imaging train, and other factors.To maximize image quality, one must ensure that each sub-exposure is aligned to a common frame of reference prior to stacking.This is done by reprojecting each exposure onto a common target grid defined using a World Coordinate System (WCS) that is defined by mapping the known angular positions of reference objects to their observed spatial positions on each image.The transformations needed to reproject images involve complicated trigonometric expressions which can be slow to compute, so reprojection can be a major bottleneck in image processing pipelines.To make astronomical reprojections faster to implement in pipelines, we have written dfreproject, a Python package of GPU-optimized functions for this purpose.The package's functions break down coordinate transformations using gnomonic projections to define pixel-by-pixel shifts from the source to the target plane.The package also provides tools for interpolating a source image onto a target plane with a single function call.This module follows the FITS and SIP formats laid out by Greisen & Calabretta (2002), Calabretta & Greisen (2002), and Shupe et al. (2005).Compared to common alternatives, dfreproject's routines result in speedups of up to 20x when run on a GPU and 10x when run on a CPU.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.103 | 0.071 |
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".