A random forest spectral classification of the
Bibliographic record
Abstract
Context. The third Gaia Data Release (Gaia DR3) has provided the astronomical community with astrometric data on more than 1.8 billion sources, along with low-resolution spectra for 220 million of them. Such a large amount of data is difficult to handle by means of visual inspection. In recent years, artificial intelligence and machine learning algorithms have started to be applied in astronomy for data analysis and automatic classification, with excellent results. Aims. In this work, we present a spectral analysis of the Gaia white dwarf population up to 500 pc from the Sun based on artificial intelligence algorithms to classify the sample into their main spectral types and subtypes. Methods. In order to classify the sample, which consists of 78 920 white dwarfs with available Gaia spectra, we have applied a random forest (RF) algorithm to the Gaia spectral coefficients. We used the Montreal White Dwarf Database of previously labeled objects as our training sample. We compared this classified sample with other already published catalogs and with our own higher resolution Gran Telescopio Canarias (GTC) spectra. This allowed us to construct a golden sample of well-classified objects. Results. The RF spectral classification of the 500 pc white dwarf population achieved an excellent global accuracy of 0.91 and an F1-score of 0.88 for the DA classification (i.e., white dwarfs that show Balmer spectral lines) versus the non-DA classification. In addition, we obtained a very high accuracy of 0.76 and a global F1-score of 0.62 for the non-DA subtype classification. In particular, our classification shows an excellent recall for DAs, as well as DBs and DCs (>90%), along with a very good precision (≥80%) for DQs, DZs, and DOs. Unfortunately, our algorithm does not perform as well with respect to correctly classifying subtypes due to the low resolution of the Gaia spectra. Conclusions. The use of machine learning techniques, in particular, the RF algorithm, has enabled us to spectrally classify 78 920 white dwarfs – an increase of 543.6% over those previously labeled – with reasonable accuracy. Having an estimate of the spectral type for the vast majority of white dwarfs up to 500 pc provides the possibility of making better estimates of cooling ages, star formation rates, and stellar evolution processes, among other fundamental aspects necessary for studying the white dwarf population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.003 | 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".