Precise stellar surface gravities from the time scales of convectively driven brightness variations
Bibliographic record
Abstract
A significant part of the intrinsic brightness variations in cool stars of low and intermediate mass arises from surface convection (seen as granulation) and acoustic oscillations (p-mode pulsations). The characteristics of these phenomena are largely determined by the stars' surface gravity (g). Detailed photometric measurements of either signal can yield an accurate value of g. However, even with ultraprecise photometry from NASA's Kepler mission, many stars are too faint for current methods or only moderate accuracy can be achieved in a limited range of stellar evolutionary stages. This means that many of the stars in the Kepler sample, including exoplanet hosts, are not sufficiently characterized to fully describe the sample and exoplanet properties. We present a novel way to measure surface gravities with accuracies of about 4%. Our technique exploits the tight relation between g and the characteristic time scale of the combined granulation and p-mode oscillation signal. It is applicable to all stars with a convective envelope, including active stars. It can measure g in stars for which no other analysis is now possible. Because it depends on the time scale (and no other properties) of the signal, our technique is largely independent of the type of measurement (for example, photometry or radial velocity measurements) and the calibration of the instrumentation used. However, the oscillation signal must be temporally resolved; thus, it cannot be applied to dwarf stars observed by Kepler in its long-cadence mode.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".