Improving Gyrochronology: a new benchmark data set and age inference model
Bibliographic record
Abstract
Gyrochronology uses a star’s rotation period and location on the main sequence (MS) to predict stellar age. It is particularly useful for low mass main sequence stars, the regime in which other stellar dating methods (e.g. isochrone fitting) tend to struggle. Gyrochronology has gained popularity in recent years due to the increasing availability of photometric data, but analytical models have struggled to coherently summarize the uncertainty and intrinsic variance in the photometric time series data. Thus, we have developed a generalized machine learning-based Bayesian inference framework that captures the uncertainty and variance through a probabilistic solution. Our framework implements a normalizing flow -- a neural network-based model that optimizes the transformation of parameter distributions -- to predict rotation period distributions for stars based on their age and colour. We have successfully trained and tested the model on data from eight open clusters with promising results, indicating that a data-driven approach to gyrochronology could improve upon existing models. We have now expanded and standardized our data sample to 30 open clusters; in this talk we will present the results of cluster age recovery testing using this new model.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".