FORMATION OF BLACK HOLE X-RAY BINARIES IN GLOBULAR CLUSTERS
Bibliographic record
Abstract
Inspired by the recent identification in extragalactic globular clusters of the first candidate black hole–white dwarf (BH–WD) X-ray binaries, where the compact accretors may be stellar-mass black holes (BHs), we explore how such binaries could be formed in a dynamical environment. We provide analyses of the formation rates via well-known formation channels like binary exchange and physical collisions and propose that the only possibility of forming BH–WD binaries is via coupling these usual formation channels with subsequent hardening and/or triple formation. In particular, we find that the most important mechanism for the creation of a BH–WD X-ray binary from an initially dynamically formed BH–WD binary is mass transfer induced in a triple system via the Kozai mechanism. Furthermore, we find that BH–WD binaries that evolve into X-ray sources can be formed by exchanges of a BH into a WD–WD binary or possibly by collisions of a BH and a giant star. If BHs undergo significant evaporation from the cluster or form a completely detached subcluster of BHs, then we cannot match the observationally inferred production rates even using the most optimistic estimates of formation rates. To explain the observations with stellar-mass BH–WD binaries, at least 1% of all formed BHs, or presumably 10% of the BHs present in the core now, must be involved in interactions with the rest of the core stellar population.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".