The chemistry of extra-solar materials from white dwarf planetary systems
Bibliographic record
Abstract
White dwarf planetary systems provide a unique way to measure the bulk composition of exoplanetary material. Extrasolar asteroids/comets/moons which have survived the evolution of their host star can end up in the atmosphere of the white dwarf. Asteroids and boulders appear to be the most common pollutants, where we use the term "asteroids" to refer to the parent body that is polluting the atmosphere. The presence of the planetary material is detected via absorption lines of heavy elements. White dwarfs with these absorption features are called "polluted" white dwarfs. Polluted white dwarfs were expected to be rare objects because white dwarfs have high surface gravities, therefore, these heavy elements will settle out of the white dwarf's atmospheres in a short amount of time (Paquette et al. 1986). However, high-resolution spectroscopic surveys found that 25-50% of white dwarfs are polluted (Zuckerman et al. 2003, 2010; Koester et al. 2014). The mechanism responsible for making a polluted white dwarf must be common and efficient. There is strong theoretical and observational evidence that white dwarfs are accreting from planetary material. There are different mechanisms that can deliver exoplanetary material into the Roche lobe of the white dwarf. Debris disks, transits from disintegrating bodies, and intact planets have all been detected around white dwarfs (e.g., Jura et al. 2007; Vanderburg et al. 2015, 2020). This chapter will describe how the chemical autopsies are conducted, and what is learnt about exoplanetary material from polluted white dwarfs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".