OSCILLATING RED GIANTS OBSERVED DURING CAMPAIGN 1 OF THE <i>KEPLER</i> K2 MISSION: NEW PROSPECTS FOR GALACTIC ARCHAEOLOGY
Bibliographic record
Abstract
NASA's re-purposed Kepler mission—dubbed K2—has brought new scientific opportunities that were not anticipated for the original Kepler mission. One science goal that makes optimal use of K2's capabilities, in particular its 360° ecliptic field of view, is galactic archaeology—the study of the evolution of the Galaxy from the fossil stellar record. The thrust of this research is to exploit high-precision, time-resolved photometry from K2 in order to detect oscillations in red giant stars. This asteroseismic information can provide estimates of stellar radius (hence distance), mass, and age of vast numbers of stars across the Galaxy. Here we present the initial analysis of a subset of red giants, observed toward the north galactic gap, during the mission's first full science campaign. We investigate the feasibility of using K2 data for detecting oscillations in red giants that span a range in apparent magnitude and evolutionary state (hence intrinsic luminosity). We demonstrate that oscillations are detectable for essentially all cool giants within the range ∼1.9–3.2. Our detection is complete down to , which results in a seismic sample with little or no detection bias. This sample is ideally suited to stellar population studies that seek to investigate potential shortcomings of contemporary Galaxy models.
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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 | 0.001 | 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) | 0.001 | 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".