3D spectroscopic analysis of helium-line white dwarfs
Bibliographic record
Abstract
ABSTRACT In this paper, we present corrections to the spectroscopic parameters of DB and DBA white dwarfs with −10.0 ≤ log (H/He) ≤ −2.0, 7.5 ≤ log g ≤ 9.0, and $12\, 000$ ≲ Teff $\lesssim 34\, 000\,\mathrm{ K}$, based on 282 3D atmospheric models calculated with the co5bold radiation-hydrodynamics code. These corrections arise due to a better physical treatment of convective energy transport in 3D models when compared to the previously available 1D model atmospheres. By applying the corrections to an existing Sloan Digital Sky Survey (SDSS) sample of DB and DBA white dwarfs, we find significant corrections both for effective temperature and surface gravity. The 3D log g corrections are most significant for Teff ≲ 18 000 K, reaching up to −0.20 dex at log g = 8.0. However, in this low effective temperature range, the surface gravity determined from the spectroscopic technique can also be significantly affected by the treatment of the neutral van der Waals line broadening of helium and by non-ideal effects due to the perturbation of helium by neutral atoms. Thus, by removing uncertainties due to 1D convection, our work showcases the need for improved description of microphysics for DB and DBA model atmospheres. Overall, we find that our 3D spectroscopic parameters for the SDSS sample are generally in agreement with Gaia Data Release 2 absolute fluxes within 1σ–3σ for individual white dwarfs. By comparing our results to DA white dwarfs, we determine that the precision and accuracy of DB/DBA atmospheric models are similar. For ease of user application of the correction functions, we provide an example python code.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".