The <i>Gaia</i> Catalogue of Galactic AGB Stars
Bibliographic record
Abstract
Context. The Gaia mission discovered several hundred thousand long-period variables and measured parallaxes for many of them. These stars will allow us to study populations of variable stars in the Milky Way, including asymptotic giant branch (AGB) stars. Aims. This paper describes the identification of Gaia counterparts of a sample of oxygen-rich AGB stars with OH maser emission as a first step towards the compilation of a general Gaia Catalogue of Galactic AGB stars. With this catalogue, tests of evolutionary models for the AGB star population in the solar neighbourhood become feasible. Methods. We cross-matched AGB star candidates showing OH maser emission with the Gaia DR3 release using a cross-match with AllWISE and 2MASS as intermediate steps to avoid ambiguities. With the help of the Virtual Observatory, we retrieved photometric data from the near-ultraviolet to the far-infrared and built spectral energy distributions (SEDs) of the sources. The SEDs were fitted with theoretical models. The fit results, together with information from the literature, allowed us to clean the sample from non-AGB stars. For the AGB stars, bolometric fluxes were obtained. Distances based on Gaia parallaxes were used to derive the stellar luminosities. Results. We identified unique Gaia counterparts for 1487 OH masers. Of these, 1172 had an unambiguous classification as AGB stars. These sources make up the Gaia OH/IR star sample. Parallaxes with relative errors < 20% and astrometric excess noise < 1.5 mas were available for 222 OH/IR stars. Conclusions. The study of the AGB population in the solar neighbourhood is limited by the obscuration by circumstellar dust, as Gaia DR3 only provides parallaxes for a few of our candidates. The location of the OH/IR stars matches that of LPV discovered by Gaia in the (BP–RP; Gabs) diagram, but the OH/IR star sample is biased towards redder colours (BP–RP > 4) mag and larger amplitudes (> 1 mag in the G-band), which are typical for periodic large-amplitude Mira variables.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".