Making do Without Spectroscopy: Using Computational Models to Uncover Parameter Correlations in Binary Stars
Bibliographic record
Abstract
The analysis of eclipsing binary star systems provides one of the most reliable methods of accurately estimating physical stellar parameters. However, when given photometric observations of a binary, the process of identifying which parameters best describe that system often proves difficult. This issue is further compounded for faint star systems where it is challenging to obtain stellar spectra. Here we present modelling of three binary systems observed with Kepler using the Physics of Eclipsing Binaries (PHOEBE) software package. Markov Chain Monte Carlo methods were used to evaluate the goodness of fit of our synthetic observables to photometric data gathered by the Kepler mission. We also seek to demonstrate the feasibility of constraining physical parameters using only photometric data, especially for those systems where spectroscopic data is difficult to obtain. Although we cannot completely constrain the parameters of systems for which we lack spectroscopic information, MCMC analysis provides knowledge of the correlations between the parameters of our model. We will discuss our findings along with the limits of what we can achieve using only photometric data.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".