Dwarfs or Giants? Stellar Metallicities and Distances from ugrizG Multiband Photometry
Bibliographic record
Abstract
Abstract We present a new fully data-driven algorithm that uses photometric data from the Canada–France Imaging Survey (CFIS; u), Pan-STARRS 1 (PS1; griz), and Gaia (G) to discriminate between dwarf and giant stars and to estimate their distances and metallicities. The algorithm is trained and tested using the Sloan Digital Sky Survey (SDSS)/SEGUE spectroscopic data set and Gaia photometric/astrometric data set. At [Fe/H] < −1.2, the algorithm succeeds in identifying more than 70% of the giants in the training/test set, with a dwarf contamination fraction below 30% (with respect to the SDSS/SEGUE data set). The photometric metallicity estimates have uncertainties better than 0.2 dex when compared with the spectroscopic measurements. The distances estimated by the algorithm are valid out to a distance of at least ∼80 kpc without requiring any prior on the stellar distribution and have fully independent uncertainties that take into account both random and systematic errors. These advances allow us to estimate these stellar parameters for approximately 12 million stars in the photometric data set. This will enable studies involving the chemical mapping of the distant outer disk and the stellar halo, including their kinematics using the Gaia proper motions. This type of algorithm can be applied in the southern hemisphere to the first release of LSST data, thus providing an almost complete view of the external components of our Galaxy out to at least ∼80 kpc. Critical to the success of these efforts will be ensuring well-defined spectroscopic training sets that sample a broad range of stellar parameters with minimal biases. A catalog containing the training/test set and all relevant parameters within the public footprint of CFIS is available online.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".