Bayesian Rhapsody: The BASTA Way to Fit a Star
Bibliographic record
Abstract
Understanding fundamental stellar properties – such as mass, radius, and age – is essential for studies of stellar evolution, exoplanet host characterization, and Galactic archaeology. The BAyesian STellar Algorithm (BASTA) is an open-source software package designed to perform precise and robust stellar inference by combining observables from asteroseismology, spectroscopy, photometry, and astrometry. BASTA offers a complete analysis pipeline tailored to oscillating main-sequence, subgiant, and red giant stars, but is also applicable when asteroseismic data are unavailable. It has been successfully used in over 150 published scientific studies and has been selected as the main foundation for the asteroseismic inference modules for the PLATO stellar pipeline. In this poster, we showcase BASTA’s latest features, highlight recent student-led work, and preview upcoming developments planned for release in 2025. This includes a redesigned user interface for improved accessibility (even with minimal Python experience), enhanced flexibility in the likelihood computation, and improved interpolation across stellar model grids. With these developments, BASTA continues to grow as a powerful, user-friendly tool for the stellar community.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.080 | 0.058 |
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".