An All-sky Survey of White Dwarf Merger Remnants: Far-Ultraviolet Is the Key
Bibliographic record
Abstract
Abstract The majority of merging white dwarfs leave behind a white dwarf remnant. Hot/warm DQ white dwarfs with carbon-rich atmospheres have high masses and unusual kinematics. All evidence points to a merger origin. Here, we demonstrate that far-UV (FUV) and optical photometry provides an efficient way to identify these merger remnants. We take advantage of this photometric selection to identify 167 candidates in the Galaxy Evolution Explorer All-Sky Imaging Survey footprint, and provide follow-up spectroscopy. Out of the 140 with spectral classifications, we identify 75 warm DQ white dwarfs with T eff > 10,000 K, nearly tripling the number of such objects known. Our sample includes 13 DAQ white dwarfs with spectra dominated by hydrogen and (weaker) carbon lines. Ten of these are new discoveries, including the hottest DAQ known to date, with T eff ≈ 23,000 K and M = 1.31M ⊙. We provide a model atmosphere analysis of all warm DQ white dwarfs found, and present their temperature and mass distributions. The sample mean and standard deviation are T eff = 14,560 ± 1970 K and M = 1.11 ± 0.09M ⊙. Warm DQs are roughly twice as massive as the classical DQs found at cooler temperatures. All warm DQs are found on or near the crystallization sequence. Even though their estimated cooling ages are of order 1 Gyr, their kinematics indicate an origin in the thick disk or halo. Hence, they are likely stuck on the crystallization sequence for ∼10 Gyr due to significant cooling delays from distillation of neutron-rich impurities. Future all-sky FUV surveys like Ultraviolet Explorer have the potential to significantly expand this sample.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".