Bibliographic record
Abstract
Context. Currently, galactic exploration is being revolutionized by a flow of new data: Gaia provides measurements of stellar distances and kinematics; growing numbers of spectroscopic surveys provide values of stellar atmospheric parameters and abundances of elements; and Kepler and K2 missions provide asteroseismic information for an increasing number of stars. Aims. In this work, we aim to determine stellar distances and ages using Gaia and spectrophotometric data in a consistent way. We estimate precisions of age and distance determinations with Gaia end-of-mission (EoM) and Tycho-Gaia astrometric solution (TGAS) parallax precisions. Methods. To this end, we incorporated parallax and extinction data into the isochrone fitting method used in the Unified tool to estimate Distances, Ages, and Masses (UniDAM). We prepared datasets that allowed us to study the improvement of distance and age estimates with the inclusion of TGAS and Gaia EoM parallax precisions in isochrone fitting. Results. Using TGAS parallaxes in isochrone fitting, we are able to reduce distance and age estimate uncertainties for TGAS stars for distances up to 1 kpc by more than one third compared to results based only on spectrophotometric data. With Gaia EoM parallaxes in isochrone fitting, we will be able to further decrease our distance uncertainties by about a factor of 20 and age uncertainties by a factor of 2 for stars up to 10 kpc away from the Sun. Conclusions. We demonstrate that we will be able to improve our distance estimates for about one third of stars in spectroscopic surveys and to decrease log(age) uncertainties by about a factor of two for over 80% of stars as compared to the uncertainties obtained without parallax priors using Gaia EoM parallaxes consistently with spectrophotometry in isochrone fitting.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".