THE ELM SURVEY. IV. 24 WHITE DWARF MERGER SYSTEMS
Bibliographic record
Abstract
We present new radial velocity and X-ray observations of extremely low mass (ELM, ∼0.2 M ☉ ) white dwarf (WD) candidates in the Sloan Digital Sky Survey Data Release 7 area. We identify seven new binary systems with 1–18 hr orbital periods. Five of the systems will merge due to gravitational wave radiation within 10 Gyr, bringing the total number of merger systems found in the ELM Survey to 24. The ELM Survey has now quintupled the known merger WD population. It has also discovered the eight shortest period detached binary WD systems currently known. We discuss the characteristics of the merger and non-merger systems observed in the ELM Survey, including their future evolution. About half of the systems have extreme mass ratios. These are the progenitors of the AM Canum Venaticorum systems and Type Ia supernovae. The remaining targets will lead to the formation of extreme helium stars, subdwarfs, or massive WDs. We identify three targets that are excellent gravitational wave sources. These should be detected by the Laser Interferometer Space Antenna like missions within the first year of operation. The remaining targets are important indicators of what the Galactic foreground may look like for gravitational wave observatories.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".