Confirmation of the Gaia DR2 Parallax Zero-point Offset Using Asteroseismology and Spectroscopy in the Kepler Field
Bibliographic record
Abstract
Abstract We present an independent confirmation of the zero-point offset of Gaia Data Release 2 parallaxes using asteroseismic data of evolved stars in the Kepler field. Using well-characterized red giant branch stars from the APOKASC-2 catalog, we identify a Gaia astrometric pseudocolor ( ν eff )- and Gaia G-band magnitude-dependent zero-point offset of ϖ seis − ϖ Gaia = 52.8 ± 2.4 (rand.) ± 8.6 (syst.) − (150.7 ± 22.7)( ν eff − 1.5) − (4.21 ± 0.77)(G − 12.2) μas, in the sense that Gaia parallaxes are too small. The offset is found in high- and low-extinction samples, as well as among both shell H-burning red giant stars and core He-burning red clump stars. We show that errors in the asteroseismic radius and temperature scales may be distinguished from errors in the Gaia parallax scale. We estimate systematic effects on the inferred global Gaia parallax offset, c, due to radius and temperature systematics, as well as choices in bolometric correction and the adopted form for Gaia parallax spatial correlations. Because of possible spatially correlated parallax errors, as discussed by the Gaia team, our Gaia parallax offset model is specific to the Kepler field, but broadly compatible with the magnitude- and color-dependent offset inferred by the Gaia team and several subsequent investigations using independent methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".