The PAndAS View of the Andromeda Satellite System. III. Dwarf Galaxy Detection Limits
Bibliographic record
Abstract
Abstract We determine the detection limits of the search for dwarf galaxies in the Pan-Andromeda Archaeological Survey (PAndAS) using the algorithm developed by the PAndAS team. The recovery fractions of artificial dwarf galaxies are, as expected, a strong function of physical size and luminosity and, to a lesser extent, distance. We show that these recovery fractions vary strongly with location in the surveyed area because of varying levels of contamination from both the Milky Way foreground stars and the stellar halo of Andromeda. We therefore provide recovery fractions that are a function of size, luminosity, and location within the survey on a scale of ∼1 × 1 deg2 (or ∼14 × 14 kpc2). Overall, the effective surface brightness for a 50% detection rate ranges between 28 and 30 mag arcsec−2. This is in line with expectations for a search that relies on photometric data that are as deep as the PAndAS survey. The derived detection limits are an essential ingredient on the path to constraining the global properties of Andromeda’s system of satellite dwarf galaxies and, more broadly, to providing constraints on dwarf galaxy formation and evolution in a cosmological context.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".