The loudest stellar heartbeat: characterizing the most extreme amplitude heartbeat star system
Bibliographic record
Abstract
ABSTRACT We characterize the extreme heartbeat star system MACHO 80.7443.1718 in the Large Magellanic Cloud using Transiting Exoplanet Survey Satellite (TESS) photometry and spectroscopic observations from the Magellan Inamori Kyocera Echelle (MIKE) and SOAR Goodman spectographs. MACHO 80.7443.1718 was first identified as a heartbeat star system in the All-Sky Automated Survey for SuperNovae (ASAS-SN) with $P_{\rm orb}=32.836\pm 0.008\, {\rm d}$. MACHO 80.7443.1718 is a young (∼6 Myr), massive binary, composed of a B0 Iae supergiant with $M_1 \simeq 35\, {\rm M}_\odot$ and an O9.5V secondary with $M_2 \simeq 16\, {\rm M}_\odot$ on an eccentric (e = 0.51 ± 0.03) orbit. In addition to having the largest variability amplitude amongst all known heartbeats stars, MACHO 80.7443.1718 is also one of the most massive heartbeat stars yet discovered. The B[e] supergiant has Balmer emission lines and permitted/forbidden metallic emission lines associated with a circumstellar disc. The disc rapidly dissipates at periastron that could indicate mass transfer to the secondary, but re-emerges immediately following periastron passage. MACHO 80.7443.1718 also shows tidally excited oscillations at the N = 25 and N = 41 orbital harmonics and has a rotational period of 4.4 d.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".