THE GHOSTS SURVEY. I. <i>HUBBLE SPACE TELESCOPE</i> ADVANCED CAMERA FOR SURVEYS DATA
Bibliographic record
Abstract
We present an overview of the GHOSTS survey, the largest study to date of the resolved stellar populations in the outskirts of disk galaxies. The sample consists of 14 disk galaxies within 17 Mpc, whose outer disks and halos are imaged with the Hubble Space Telescope Advanced Camera for Surveys (ACS). In the first paper of this series, we describe the sample, explore the benefits of using resolved stellar populations, and discuss our ACS F606W and F814W photometry. We use artificial star tests to assess completeness and use overlapping regions to estimate photometric uncertainties. The median depth of the survey at 50% completeness is 2.7 mag below the tip of the red giant branch (TRGB). We comprehensively explore and parameterize contamination from unresolved background galaxies and foreground stars using archival fields of high-redshift ACS observations. Left uncorrected, these would account for 10 0.65 × F814W − 19.0 detections per mag per arcsec 2 . We therefore identify several selection criteria that typically remove 95% of the contaminants. Even with these culls, background galaxies are a significant limitation to the surface brightness detection limit which, for this survey, is typically V ∼ 30 mag arcsec −2 . The resulting photometric catalogs are publicly available and contain some 3.1 million stars across 76 ACS fields, predominantly of low extinction. The uniform magnitudes of TRGB stars in these fields enable galaxy distance estimates with 2%–7% accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".