A search for black holes with metal-poor stellar companions: I. Survey sample selection and single epoch radial velocity follow-up
Bibliographic record
Abstract
Stellar-mass black holes (BHs) above $30 M_\odot$ are predicted to form from low-metallicity progenitors, but direct detections of such systems in the Milky Way remain scarce. Motivated by the recent discovery of Gaia BH3, a $33 M_\odot$ BH with a very metal-poor giant companion, we conduct a systematic search for additional systems. Approximately 900 candidates are identified with Gaia as having significant deviations from single-star astrometric motion, evidence of RV variability, and low metallicities inferred from Gaia XP spectra. We obtain single epoch high-resolution spectra for over 600 of these sources with Magellan/MIKE and Lick/APF and measure independent RVs with $\approx 1$ km s$^{-1}$ precision. After removing contaminants such as hot stars, pulsators, eclipsing binaries, and hierarchical triples, we identify about 15 promising candidates with large RV amplitudes or offsets from the Gaia reported values. This program establishes a well-characterized sample of BH candidates for detailed orbital modeling once Gaia DR4 epoch astrometry and RVs are released in late 2026; multi-epoch RV follow-up is ongoing. Together, the Gaia and ground-based data will place new constraints on the demographics of BHs with metal-poor companions and test theoretical predictions linking low metallicity to the formation of the most massive stellar remnants.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".