New eclipsing binaries with mercury–manganese stars
Bibliographic record
Abstract
ABSTRACT Eclipsing binary stars are rare and extremely valuable astrophysical laboratories that make possible precise determination of fundamental stellar parameters. Investigation of early-type chemically peculiar stars in eclipsing binaries provides important information for understanding the origin and evolutionary context of their anomalous surface chemistry. In this study, we discuss observations of eclipse variability in six mercury–manganese (HgMn) stars monitored by the Transiting Exoplanet Survey Satellite (TESS) satellite. These discoveries double the number of known eclipsing HgMn stars and yield several interesting objects requiring further study. In particular, we confirm eclipses in HD 72208, thereby establishing this object as the longest-period eclipsing HgMn star. Among five other eclipsing binaries, reported here for the first time, HD 36892 and HD 53004 stand out as eccentric systems showing heartbeat variability in addition to eclipses. The latter object has the highest eccentricity among eclipsing HgMn stars and also exhibits tidally induced oscillations. Finally, we find evidence that HD 55776 may be orbited by a white dwarf companion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".