Lower-mass-gap Black Holes in Dense Star Clusters
Bibliographic record
Abstract
Abstract The existence of compact stellar remnants in the mass range 2–5 M ⊙ has long been debated. This so-called lower-mass gap (LMG) was initially suggested by the lack of low-mass X-ray binary observations with accretors about 2–5 M ⊙, but it has recently been called into question following newer observations, including an LMG candidate with a millisecond pulsar (MSP) companion in the dense globular cluster NGC 1851. Here, we model NGC 1851 with a grid of similar dense star clusters utilizing the state-of-the-art Monte Carlo N-body code Cluster Monte Carlo, and we specifically study the formation of LMG black holes (BHs). We demonstrate that both massive star evolution and dynamical interactions can contribute to forming LMG BHs. In general, the collapse of massive remnants formed through mergers of neutron stars (NSs) or massive white dwarfs produces the largest number of LMG BHs among all formation channels. However, in more massive clusters, supernova core collapse can contribute comparable numbers. Our NGC 1851-like models can reproduce MSP—LMG BH binaries similar to the observed system. Additionally, the LMG BHs can also become components of dynamically assembled binaries, and some will be in merging BH–NS systems similar to the recently detected gravitational wave source GW230529. However, the corresponding merger rate is probably ≲1 Gpc−3 yr−1.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".