Discovery and characterisation of long-period eclipsing binary stars from <i>Kepler</i> K2 campaigns 1, 2, and 3
Bibliographic record
Abstract
Context. The Kepler K2 mission now makes it possible to find and study a wider variety of eclipsing binary stars than has been possible to-date, particularly long-period systems with narrow eclipses. Aims. Our aim is to characterise eclipsing binary stars observed by the Kepler K2 mission with orbital periods longer than P ≈ 5.5 days. Methods. The ellc binary star model has been used to determine the geometry of eclipsing binary systems in Kepler K2 campaigns 1, 2 and 3. The nature of the stars in each binary is estimated by comparison to stellar evolution tracks in the effective temperature – mean stellar density plane. Results. 43 eclipsing binary systems have been identified and 40 of these are characterised in some detail. The majority of these systems are found to be late-type dwarf and sub-giant stars with masses in the range 0.6–1.4 solar masses. We identify two eclipsing binaries containing red giant stars, including one bright system with total eclipses that is ideal for detailed follow-up observations. The bright B3V-type star HD 142883 is found to be an eclipsing binary in a triple star system. We observe a series of frequencies at large multiples of the orbital frequency in BW Aqr that we tentatively identify as tidally induced pulsations in this well-studied eccentric binary system. We find that the faint eclipsing binary EPIC 201160323 shows rapid apsidal motion. Rotational modulation signals are observed in 13 eclipsing systems, the majority of which are found to rotate non-synchronously with their orbits. Conclusions. The K2 mission is a rich source of data that can be used to find long period eclipsing binary stars. These data combined with follow-up observations can be used to precisely measure the masses and radii of stars for which such fundamental data are currently lacking, e.g., sub-giant stars and slowly-rotating low-mass stars.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".