Bibliographic record
Abstract
ABSTRACT Observations point to old white dwarfs (WDs) accreting metals at a relatively constant rate over 8 Gyr. Exo-Oort clouds around WDs have been proposed as potential reservoirs of materials, with galactic tide as a mechanism to deliver distant comets to the WD’s Roche limit. In this work, we characterize the dynamics of comets around a WD with a companion having semimajor axes on the orders of 10–100 au. We develop simulation techniques capable of integrating a large number (108) of objects over a 1 Gyr time-scale. Our simulations include galactic tide and are capable of resolving close interactions with a massive companion. Through simulations, we study the accretion rate of exo-Oort cloud comets into a WD’s Roche limit. We also characterize the dynamics of precession and scattering induced on a comet by a massive companion. We find that (i) WD pollution by an exo-Oort cloud can be sustained over a Gyr time-scale, (ii) an exo-Oort cloud with structure like our own Solar system’s is capable of delivering materials into an isolated WD with pollution rate ∼108 g s−1, (iii) adding a planetary-mass companion reduces the pollution rate to ∼107 g s−1, and (iv) if the companion is stellar mass, with Mp ≳ 0.1 M⊙, the pollution rate reduces to ∼3 × 105 g s−1 due to a combination of precession induced on a comet by the companion, a strong scattering barrier, and low likelihood of direct collisions of comets with the companion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".