Weak Gravitational Lensing with COSMOS: Galaxy Selection and Shape Measurements
Bibliographic record
Abstract
With a primary goal of conducting precision weak-lensing measurements from space, the COSMOS survey has imaged the largest contiguous area observed by Hubble Space Telescope to date, using the Advanced Camera for Surveys (ACS). This is the first paper in a series in which we describe our strategy for addressing the various technical challenges in the production of weak-lensing measurements from COSMOS data. We first construct a source catalog from 575 ACS/WFC tiles (1.64 deg 2 ) subsampled at a pixel scale of 0.03''. Defects and diffraction spikes are carefully removed, leaving a total of 1.2 × 10 6 objects to a limiting magnitude of F814W = 26.5. This catalog is made publicly available. Multiwavelength follow-up observations of the COSMOS field provide photometric redshifts for 73% of the source galaxies in the lensing catalog. We analyze and discuss the COSMOS redshift distribution and show broad agreement with other surveys to z ~ 1. Our next step is to measure the shapes of galaxies and correct them for the distortion induced by the time-varying ACS point-spread function and for charge transfer efficiency (CTE) effects. Simulated images are used to derive the shear susceptibility factors that are necessary in transforming shape measurements into unbiased shear estimators. For every galaxy we derive a shape measurement error and utilize this quantity to extract the intrinsic shape noise of the galaxy sample. Interestingly, our results indicate that intrinsic shape noise varies little with size, magnitude, or redshift. Representing a number density of 66 galaxies per arcmin 2 , the final COSMOS weak-lensing catalog contains 3.9 × 10 5 galaxies with accurate shape measurements. The properties of the COSMOS weak-lensing catalog described throughout this paper will provide key input numbers for the preparation and design of next-generation wide field space missions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".