Stellar Age Inference with Rotation (+ Activity?)
Bibliographic record
Abstract
Ages of low mass main sequence (MS) stars can be challenging to measure because of their long MS lifetimes. While gyrochronology (a method of dating such stars using rotation period) is somewhat effective, the observed dispersion in rotation rates for similar coeval stars has historically been difficult to model. To characterize this complexity, we present the largest standardized catalogue of rotators in open clusters to date, which we have used to develop ChronoFlow: a state-of-the-art machine learning framework that can be used to forward model rotational evolution and to infer stellar ages. Additionally, we present the results of robust systematic tests in which we quantify the impact of extinction models, cluster membership, and calibration techniques on age estimates. Building on this, we explore whether other manifestations of magnetic activity in Kepler/K2/TESS light curves (such as photometric variability and flaring) can provide age information that is complementary to rotation, and we test whether joint activity-rotation models can constrain stellar ages better than rotation alone.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".