A comprehensive look at RR Lyrae stars through high-precision photometry and astrometry
Bibliographic record
Abstract
The TESS and Gaia missions are both collecting data of exquisite quality for RR Lyrae-type pulsating stars. We combine light curve shape information from TESS with distances and brightnesses from Gaia to build a detailed classification scheme that is able to filter out intrinsically fainter binaries and rotational variables as well as short-period Cepheids that otherwise have light curves very similar to that of RR Lyrae stars, while preserving targets with unusual light curve shapes. We build a clean sample from the RR Lyrae stars in the vicinity of the Sun, out to 5-10 kpc distances. Based on the TESS light curves we find that a large fraction of RR Lyrae stars exhibit various additional low-amplitude modes. The distribution of extra modes in period is markedly different from that of stars within the bulge and globular clusters, signaling a metallicity dependence in the mode periods and excitation mechanisms. We also detect a possible dependence of extra modes from color and hence effective temperatures in overtone RRc stars. While these new results have brought us closer to true asteroseismic analysis of RR Lyrae stars, we show through new échelle-type diagrams that mode identification is still hindered by the strong influence of the radial mode(s) dominating the pulsation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".