Sparse Logistic Regression for RR Lyrae versus Binaries Classification
Bibliographic record
Abstract
Abstract RR Lyrae (RRL) stars are old, low-mass, radially pulsating variable stars in their core helium burning phase. They are popular stellar tracers and primary distance indicators since they obey well-defined period–luminosity relations in the near-infrared regime. Their photometric identification is not trivial; indeed, RRL star samples can be contaminated by eclipsing binaries, especially in large data sets produced by fully automatic pipelines. Interpretable machine-learning approaches for separating eclipsing binaries from RRL stars are thus needed. Ideally, they should be able to achieve high precision in identifying RRL stars while generalizing new data from different instruments. In this paper, we train a simple logistic regression classifier on Catalina Sky Survey (CSS) light curves. It achieves a precision of 87% at 78% recall for the RRL star class on unseen CSS light curves. It generalizes on out-of-sample data (ASAS/ASAS-SN light curves) with a precision of 85% at 96% recall. We also considered a L1-regularized version of our classifier, which reaches 90% sparsity in the light-curve features with a limited trade-off in accuracy on our CSS validation set and—remarkably—also on the ASAS/ASAS-SN light-curve test set. Logistic regression is natively interpretable, and regularization allows us to point out the parts of the light curves that matter the most in classification. We thus achieved both good generalization and full interpretability.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".