Using Computational Models to Uncover the Parameters of Three Kepler Binaries: KIC 5957123, KIC 8314879, and KIC 10727668*
Bibliographic record
Abstract
Abstract Theories of stellar convective core overshoot can be examined through analysis of pulsating stars. Better accuracy can be achieved by obtaining external constraints such as those provided by observing pulsating stars in eclipsing binary systems, but this requires that the binary parameters be identified so photometric variations of the pulsating component may be isolated from the binary periodicity. This study aims to uncover the physical parameters of three binaries observed by the Kepler spacecraft. We also seek to evaluate the feasibility of accurately constraining binaries using only readily available time-series photometry and distance estimates. Binary models were constructed using the Physics of Eclipsing Binaries software package. Markov Chain Monte Carlo (MCMC) methods were used to sample the parameter space of these models and provide estimates of the posterior distributions for these systems. An initial run using binned light-curve data was performed to identify general parameter trends and provide initializing distributions for a subsequent analysis incorporating the full data set. We present theoretical models for all three binaries, along with posterior distributions from our MCMC analyses. Models for KIC 8314879 and KIC 10727668 produced a good match to the observed data, while the model of KIC 5957123 failed to generate an appropriate synthetic light curve. For the two successful models, we interpret the posterior distributions and discuss confidence in our parameter estimates and uncertainties. We also evaluate the feasibility of this procedure in various contexts and propose several modifications to improve the success of future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".