Improving Gaia Parallax Precision with a Data-driven Model of Stars
Bibliographic record
Abstract
Abstract Converting a noisy parallax measurement into a posterior belief over distance requires inference with a prior. Usually, this prior represents beliefs about the stellar density distribution of the Milky Way. However, multiband photometry exists for a large fraction of the Gaia-TGAS Catalog and is incredibly informative about stellar distances. Here, we use 2MASS colors for 1.4 million TGAS stars to build a noise-deconvolved empirical prior distribution for stars in color–magnitude space. This model contains no knowledge of stellar astrophysics or the Milky Way but is precise because it accurately generates a large number of noisy parallax measurements under an assumption of stationarity; that is, it is capable of combining the information from many stars. We use the Extreme Deconvolution (XD) algorithm—which is an empirical-Bayes approximation to a full-hierarchical model of the true parallax and photometry of every star—to construct this prior. The prior is combined with a TGAS likelihood to infer a precise photometric-parallax estimate and uncertainty (and full posterior) for every star. Our parallax estimates are more precise than the TGAS catalog entries by a median factor of 1.2 (14% are more precise by a factor >2) and they are more precise than the previous Bayesian distance estimates that use spatial priors. We validate our parallax inferences using members of the Milky Way star cluster M67, which is not visible as a cluster in the TGAS parallax estimates but appears as a cluster in our posterior parallax estimates. Our results, including a parallax posterior probability distribution function for each of 1.4 million TGAS stars, are available in companion electronic tables.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".