Revisiting the Chemical Composition of WD 1145+017: Impact of Circumstellar Disk Contamination on Photospheric Abundances
Bibliographic record
Abstract
Abstract We performed a chemical analysis of the asteroid-bearing white dwarf WD 1145+017 using optical and ultraviolet spectroscopic data from 25 epochs between 2015 and 2023. We present an updated gas disk model with improved opacity calculations and temperature profiles to properly account for all circumstellar absorption features. Incorporating these changes into our models, we identified at least 11 elements in the disk, including a detection of circumstellar Na. We detected 16 elements in the photosphere, including new detections of P, Co, and Cu. At 16 elements, WD 1145+017 ties GD 362 as one of the most polluted white dwarfs in terms of the number of elements detected. We find that both the disk and photosphere compositions align, to first order, with CI Chondrite. Our study underscores the importance of accounting for circumstellar absorption, as neglecting them leads to significant abundance errors. Additionally, the analysis of the disk’s opacity highlighted an ultraviolet flux reduction due to a pseudo-continuum due to an optically thick component. This result may affect previous analyses of other polluted white dwarfs, suggesting a need for revisiting some studies.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".