The Time-domain Spectroscopic Survey: Radial Velocity Variability in Dwarf Carbon Stars
Bibliographic record
Abstract
Abstract Dwarf carbon (dC) stars (main-sequence stars showing carbon molecular bands) were initially thought to be an oxymoron because only asymptotic giant branch (AGB) stars dredge carbon into their atmospheres. Mass transfer from a former AGB companion that has since faded to a white dwarf seems the most likely explanation. Indeed, a few types of giants known to show anomalous abundances—notably, the CH, Ba and CEMP-s stars—are known to have a high binary frequency. The dC stars may be the enhanced-abundance progenitors of most, if not all of these systems, but this requires demonstrating a high binary frequency for dCs. Here, for a sample of 240 dC stars targeted for repeat spectroscopy by the SDSS-IV’s Time Domain Spectroscopic Survey, we analyze radial velocity (RV) variability to constrain the binary frequency and orbital properties. A handful of dC systems show large velocity variability (>100 km s−1). We compare the dCs to a control sample with a similar distribution of magnitude, color, proper motion, and parallax. Using Markov chain Monte Carlo methods, we use the measured ΔRV distribution to estimate the binary fraction and the separation distribution assuming both a unimodal and bimodal distribution. We find the dC stars have an enhanced binary fraction of 95%, consistent with them being products of mass transfer. These models result in mean separations of less than 1 au corresponding to periods on the order of 1 yr. Our results support the conclusion that dC stars form from close binary systems via mass transfer.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".