White dwarf Random Forest classification through <i>Gaia</i> spectral coefficients
Bibliographic record
Abstract
Context. The third data release of Gaia has provided approximately 220 million low resolution spectra. Among these, about 100 000 correspond to white dwarfs. The magnitude of this quantity of data precludes the possibility of performing spectral analysis and type determination by human inspection. In order to tackle this issue, we explore the possibility of utilising a machine learning approach, based on a Random Forest algorithm. Aims. Our goal is to analyse the viability of the Random Forest algorithm for the spectral classification of the white dwarf population within 100 pc from the Sun, based on the Hermite coefficients of Gaia spectra. Methods. We utilised the assigned spectral type from the Montreal White Dwarf Database for training and testing our Random Forest algorithm. Once validated, our algorithm model was applied to the rest of the unclassified white dwarfs within 100 pc. First, we started by classifying the two major spectral type groups of white dwarfs: hydrogen-rich (DA) and hydrogen-deficient (non-DA). Next, we explored the possibility of classifying the various spectral subtypes, including the secondary spectral types in some cases. Results. Our Random Forest classification presented a very high recall (>80%) for DA and DB white dwarfs, and a very high precision (>90%) for DB, DQ, and DZ white dwarfs. As a result we have assigned a spectral type to 9446 previously unclassified white dwarfs: 4739 DAs, 76 DBs (60 of them DBAs), 4437 DCs, 132 DZs, and 62 DQs (nine of them DQpec). Conclusions. Despite the low resolution of Gaia spectra, the Random Forest algorithm applied to the Gaia spectral coefficients proves to be a highly valuable tool for spectral classification.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".