Neural posterior estimation for white dwarf spectroscopic characterization
Bibliographic record
Abstract
ABSTRACT White dwarf spectroscopic characterization is entering a big data era, with the number of spectroscopically characterized white dwarfs expected to grow from $\sim$100 000 to over 300 000 in upcoming years. Traditional methods like least-squares fitting and Markov chain Monte Carlo have become computationally prohibitive for large-scale analysis, requiring minutes to days per star. Furthermore, these methods impose fundamental limitations on model complexity by requiring explicit likelihood functions, typically restricting them to Gaussian assumptions. We present neural posterior estimation (NPE), a simulation-based inference technique that directly approximates posterior distributions through neural networks trained on simulated spectra. Our approach provides accurate parameter inference in milliseconds per star after upfront training costs, enabling statistical tests of the procedure’s reliability. We demonstrate NPE’s effectiveness on hydrogen-, helium-, and carbon-atmosphere white dwarfs, validating its calibration with simulation-based calibration and tests of accuracy with random points. Application to SDSS data shows excellent agreement with previous studies, recovering parameters from previous work within 6.8 per cent for effective temperature and 2.1 per cent for surface gravity, on average. We also apply our technique on WD 1153+012, a hot DQ star with a carbon–oxygen–hydrogen atmosphere, using high-resolution spectroscopy. This methodology combines computational efficiency with the flexibility to model complex atmospheres, making it ideal for upcoming surveys. Our approach also integrates spectroscopic and photometric constraints through an iterative procedure, providing comprehensive characterization of white dwarfs.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".