The Stability of the Point‐Spread Function of the Advanced Camera for Surveys on the <i>Hubble Space Telescope</i> and Implications for Weak Gravitational Lensing
Bibliographic record
Abstract
We examine the spatial and temporal stability of the Hubble Space Telescope 's Advanced Camera for Surveys (ACS) Wide Field Camera (WFC) point-spread function (PSF) using the 2 deg 2 COSMOS survey. This is important for studies of weak gravitational lensing, where the ability to deconvolve the PSF from galaxy shapes is of paramount importance. We show that stochastic aliasing of the PSF necessarily occurs during "drizzling." This aliasing is maximal if the output-pixel scale is equal to the input-pixel scale. This source of PSF variation can be significantly reduced by choosing a Gaussian drizzle kernel with a size of 0.8 input pixels and by reducing the output-pixel scale. We show that the PSF is temporally unstable, resulting in an overall slow periodic focus change in the COSMOS images. Using a modified version of the Tiny Tim PSF modeling software, we create grids of undistorted stars over a range of telescope focus values. We then use the approximately 10 well-measured stars in each COSMOS field to pick the best-fit focus value for each field. The Tiny Tim model stars can then be used to perform PSF corrections for weak lensing. We derive a parametric correction for the effect of charge transfer efficiency (CTE) degradation on the shapes of objects in the COSMOS field as a function of observation date, position within the ACS WFC field, and object flux. Finally, we discuss future plans to improve the CTE correction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".