Bibliographic record
Abstract
ABSTRACT Nearby galaxies provide populations of stellar and non-stellar sources at a common distance and in quantifiable environments. All are observed through the Milky Way foreground, with varying degrees of contamination that depend on observed Galactic latitude and the distance and size of the target galaxy. This work uses Gaia Data Release 3 (DR3) to identify foreground sources via astrometric measurements and thus quantify foreground contamination for a large sample of nearby galaxies. There are approximately half a million Gaia sources in the directions of 1401 galaxies listed in the Local Volume Galaxy catalogue (D < 11 Mpc), excluding the largest Local Group galaxies. About two-thirds of the Gaia sources have astrometric properties consistent with foreground sources; these sources are brighter, redder, and less centrally concentrated than non-foreground sources. Averaged over galaxies, foreground sources make up 50 per cent of Gaia sources at projected radius r50 = 1.06a26, where a26 is the angular diameter at the B = 26.5 isophote. Foreground sources make up 50 per cent of Gaia sources at apparent magnitude mG, 50 = 20.50. This limit corresponds to the tip of the red giant branch absolute magnitude at D = 450 kpc, and to the globular cluster luminosity function peak absolute magnitude at 5 Mpc. Gaia data provide a powerful tool for removing foreground contamination in stellar population studies of nearby galaxies, although Gaia foreground removal will be incomplete beyond distances of 5 Mpc.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".