99 oscillating red-giant stars in binary systems with NASA TESS and NASA<i>Kepler</i>identified from the SB9-Catalogue
Bibliographic record
Abstract
Oscillating red-giant stars in binary systems are an ideal testbed for investigating the structure and evolution of stars in the advanced phases of evolution. With 83 known red giants in binary systems, of which only ∼40 have determined global seismic parameters and orbital parameters, the sample is small compared to the numerous known oscillating stars. The detection of red-giant binary systems is typically obtained from the signature of stellar binarity in space photometry. The time base of such data biases the detection towards systems with shorter periods and orbits of insufficient size to allow a red giant to fully extend as it evolves up the red-giant branch. Consequently, the sample shows an excess of H-shell burning giants while containing very few stars in the He-core burning phase. From the ninth catalogue of spectroscopic binary orbits (SB9), we identified candidate systems hosting a red-giant primary component. Searching space photometry from the NASA missionsKepler, K2, and TESS (Transiting Exoplanet Survey Satellite) as well as the BRITE (BRIght Target Explorer) constellation mission, we find 99 systems, which were previously unknown to host an oscillating giant component. The revised search strategy allowed us to extend the range of orbital periods of systems hosting oscillating giants up to 26 000 days. Such wide orbits allow a rich population of He-core burning primaries, which are required for a complete view of stellar evolution from binary studies. Tripling the size of the sample of known oscillating red-giant stars in binary systems is an important step towards an ensemble approach for seismology and tidal studies. While for non-eclipsing binaries the inclination is unknown, such a seismically well-characterized sample will be a treasure trove in combination withGaiaastrometric orbits for binary systems.
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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 | 0.002 | 0.001 |
| 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) | 0.003 | 0.002 |
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".