Flares in open clusters with K2
Bibliographic record
Abstract
Context. The presence and strength of a stellar magnetic field and activity is rooted in a star’s fundamental parameters such as mass and age. Can flares serve as an accurate stellar “clock”? Aims. To explore if we can quantify an activity-age relation in the form of a flaring-age relation, we measured trends in the flaring rates and energies for stars with different masses and ages. Methods. We investigated the time-domain photometry provided by Kepler’s follow-up mission K2 and searched for flares in three solar metallicity open clusters with well-known ages, M 45 (0.125 Gyr), M 44 (0.63 Gyr), and M 67 (4.3 Gyr). We updated and employed the automated flare finding and analysis pipeline Appaloosa , originally designed for Kepler . We introduced a synthetic flare injection and recovery sub-routine to ascribe detection and energy recovery rates for flares in a broad energy range for each light curve. Results. We collect a sample of 1761 stars, mostly late-K to mid-M dwarfs and found 751 flare candidates with energies ranging from 4 × 10 32 erg to 6 × 10 34 erg, of which 596 belong to M 45, 155 to M 44, and none to M 67. We find that flaring activity depends both on T eff , and age. But all flare frequency distributions have similar slopes with α ≈ 2.0−2.4, supporting a universal flare generation process. We discuss implications for the physical conditions under which flares occur, and how the sample’s metallicity and multiplicity affect our results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.000 |
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".