Weak Lensing PSF Correction of Wide-field CCD Mosaic Images (SULI Paper)
Bibliographic record
Abstract
Gravitational lensing provides some of the most compelling evidence for the existence of dark matter. Dark matter on galaxy cluster scales can be mapped due to its weak gravitational lensing effect: a cluster mass distribution can be inferred from the net distortion of many thousands of faint background galaxies that it induces. Because atmospheric aberration and defects in the detector distort the apparent shape of celestial objects, it is of great importance to characterize accurately the point spread function (PSF) across an image. In this research, the PSF is studied in images from the Canada-France-Hawaii Telescope (CFHT), whose camera is divided into 12 CCD chips. Traditional weak lensing methodology involves averaging the PSF across the entire image: in this work we investigate the effects of measuring the PSF in each chip independently. This chip-by-chip analysis was found to reduce the strength of the correlation between star and galaxy shapes, and predicted more strongly the presence of known galaxy clusters in mass maps. These results suggest correcting the CFHT PSF on an individual chip basis significantly improves the accuracy of detecting weak lensing.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".