Searching For Transiting Planets Around Halo Stars. I. Sample Selection and Validation
Bibliographic record
Abstract
Abstract By measuring the elemental abundances of a star, we can gain insight into the composition of its initial gas cloud—the formation site of the star and its planets. Planet formation requires metals, the availability of which is determined by the elemental abundance. In the case where metals are extremely deficient, planet formation can be stifled. To investigate such a scenario requires a large sample of metal-poor stars and a search for planets therein. This paper focuses on the selection and validation of a halo star sample. We select ∼17,000 metal-poor halo stars based on their Galactic kinematics, and confirm their low metallicities ([Fe/H] < −0.5), using spectroscopy from the literature. Furthermore, we perform high-resolution spectroscopic observations using LBT/PEPSI and conduct detailed metallicity ([Fe/H]) analyses on a sample of 13 previously-known halo stars that also have hot kinematics. We can use the halo star sample presented here to measure the frequency of planets and to test planet formation in extremely metal-poor environments. The result of the planet search and its implications will be presented and discussed in a companion paper by Boley et al.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".