Resolution-corrected White Dwarf Gravitational Redshifts Validate Fifth-generation Sloan Digital Sky Survey Wavelength Calibration and Enable Accurate Mass–Radius Tests
Bibliographic record
Abstract
Abstract Leveraging the large sample size of low-resolution spectroscopic surveys to constrain white dwarf stellar structure requires an accurate understanding of the shapes of hydrogen absorption lines, which are pressure broadened by the Stark effect. Using data from both the Sloan Digital Sky Survey (SDSS) and the Type Ia Supernova Progenitor Survey, we show that substantial biases (5–15 km s−1) exist in radial velocity measurements made from observations at low spectral resolution relative to similar measurements from high-resolution spectra. Our results indicate that the physics of line formation in high-density plasmas, especially in the wings of the lines, are not fully accounted for in state-of-the-art white dwarf model atmospheres. We provide corrections to account for these resolution-induced redshifts in a way that is independent of an assumed mass–radius relation, and we demonstrate that statistical measurements of gravitational redshift with these corrections yield improved agreement with theoretical mass–radius relations. Our results provide a set of best practices for white dwarf radial velocity measurements from low-resolution spectroscopy, including those from SDSS, the Dark Energy Spectroscopic Instrument, the 4 m Multi-Object Spectroscopic Telescope, and the Wide-Field Multiplexed Spectroscopic Facility.
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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 | 0.005 | 0.010 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".