Chemical Mapping of the Milky Way with The Canada–France Imaging Survey: A Non-parametric Metallicity–Distance Decomposition of the Galaxy
Bibliographic record
Abstract
Abstract We present the chemical distribution of the Milky Way, based on 2900 of u-band photometry taken as part of the Canada–France Imaging Survey. When complete, this survey will cover 10,000 of the northern sky. By combing the CFHT u-band photometry together with Sloan Digital Sky Survey and Pan-STARRS and i, we demonstrate that we are able to reliably measure the metallicities of individual stars to ∼0.2 dex, and hence additionally obtain good photometric distance estimates. This survey thus permits the measurement of metallicities and distances of the dominant main-sequence (MS) population out to approximately , and provides a much higher number of stars at large extraplanar distances than have been available from previous surveys. We develop a non-parametric distance–metallicity decomposition algorithm and apply it to the sky at and to the North Galactic Cap. We find that the metallicity–distance distribution is well-represented by three populations whose metallicity distributions do not vary significantly with vertical height above the disk. As traced in MS stars, the stellar halo component shows a vertical density profile that is close to exponential, with a scale height of around . This may indicate that the inner halo was formed partly from disk stars ejected in an ancient minor merger.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.000 |
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".