Comparing <i>Gaia</i>, NED, and SIMBAD source classifications in nearby galaxies
Bibliographic record
Abstract
ABSTRACT Gaia Data Release 3 (DR3) provides the first classifications for the sources in Gaia’s all-sky database. Most Gaia sources are stars in the Milky Way, but DR3 also contains many sources that belong to nearby galaxies, as well as background galaxies and quasars. In this work, we compare the Gaia classifications from the Discrete Source Classifier (CU8-DSC) module to the more detailed and heterogeneous classifications in NED (NASA Extragalactic Database) and/or SIMBAD (Set of Identifications, Measurements and Bibliography for Astronomical Data) for sources with sky positions within twice the Holmberg radius of nearby galaxies. Matching these catalogues gives approximately $3.2\times 10^5$ unique Gaia matches for $4\times 10^5$ sources over 1040 galaxies (excluding some large Local Group galaxies) in the Local Volume Galaxy catalogue. Matched sources contain a lower fraction of Gaia-classified stars and higher fractions of galaxies and quasars ($\sim 95$ per cent, 2 per cent, and 2 per cent, respectively) than DR3 overall. Considering NED (SIMBAD) classifications as truth values, the balanced accuracy of Gaia classification is 0.80 (0.83): the most common disagreements are literature-classified galaxies Gaia-classified as stars and literature-classified stars Gaia-classified as quasars. Purity (P) and completeness (C) metrics show that agreement between Gaia classification and NED/SIMBAD classification is best for stars ($P\, \mathrm{ and}\, C\sim 0.9$), and decreases for quasars ($P\lt 0.3$ and $0.7\lt C\lt 0.8$), galaxies ($0.7\lt P\lt 0.8$ and $0.3\lt C\lt 0.6$), white dwarfs ($0.04\lt P\lt 0.6$ and $C\sim 0.6$), and binary stars ($P\, \mathrm{ and}\, C\lt 0.1$). NED or SIMBAD sources classified only by detection wavelength are most often Gaia-classified as stars, while non-stellar components of galaxies appear in all Gaia classes.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".