The Q Branch Cooling Anomaly Can Be Explained by Mergers of White Dwarfs and Subgiant Stars
Bibliographic record
Abstract
Abstract Gaia's exquisite parallax measurements allowed for the discovery and characterization of the Q branch in the Hertzsprung–Russell diagram, where massive C/O white dwarfs (WDs) pause their dimming due to energy released during crystallization. Interestingly, the fraction of old stars on the Q branch is significantly higher than in the population of WDs that will become Q branch stars or that were Q branch stars in the past. From this, Cheng et al. inferred that ∼6% of WDs passing through the Q branch experience a much longer cooling delay than that of standard crystallizing WDs. Previous attempts to explain this cooling anomaly have invoked mechanisms involving supersolar initial metallicities. In this paper, we describe a novel scenario in which a standard composition WD merges with a subgiant star. The evolution of the resulting merger remnant leads to the creation of a large amount of 26Mg, which, along with the existing 22Ne, undergoes a distillation process that can release enough energy to explain the Q branch cooling problem without the need for atypical initial abundances. The anomalously high number of old stars on the Q branch may thus be evidence that mass transfer from subgiants to WDs leads to unstable mergers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".