Absence of a correlation between white dwarf planetary accretion and primordial stellar metallicity
Bibliographic record
Abstract
ABSTRACT Over a quarter of white dwarfs have photospheric metal pollution, which is evidence for recent accretion of exoplanetary material. While a wide range of mechanisms have been proposed to account for this pollution, there are currently few observational constraints to differentiate between them. To investigate the driving mechanism, we observe a sample of polluted and non-polluted white dwarfs in wide binary systems with main-sequence stars. Using the companion stars’ metallicities as a proxy for the white dwarfs’ primordial metallicities, we compare the metallicities of polluted and non-polluted systems. Because there is a well-known correlation between giant planet occurrence and higher metallicity (with a stronger correlation for close-in and eccentric planets), these metallicity distributions can be used to probe the role of gas giants in white dwarf accretion. We find that the metallicity distributions of polluted and non-polluted systems are consistent with the hypothesis that both samples have the same underlying metallicity distribution. However, we note that this result is likely biased by several selection effects. Additionally, we find no significant trend between white dwarf accretion rates and metallicity. These findings suggest that giant planets are not the dominant cause of white dwarf accretion events in binary systems.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".