Two of a Kind: Comparing Big and Small Black Holes in Binaries with Gravitational Waves
Bibliographic record
Abstract
Abstract When modeling the population of merging binary black holes, analyses have generally focused on characterizing the distribution of primary (i.e., more massive) black holes in the binary, while using simplistic prescriptions for the distribution of secondary masses. However, the secondary mass distribution and its relationship to the primary mass distribution provide a fundamental observational constraint on the formation history of coalescing binary black holes. If both black holes experience similar stellar evolutionary processes prior to collapse, as might be expected in dynamical formation channels, the primary and secondary mass distributions would show similar features. If they follow distinct evolutionary pathways (for example, due to binary interactions that break symmetry between the initially more massive and less massive stars), their mass distributions may differ. We present the first analysis of the binary black hole population that explicitly fits for the secondary mass distribution. We find that the data is consistent with a ∼30 M ⊙ peak existing only in the distribution of secondary rather than primary masses. This would have major implications for our understanding of the formation of these binaries. Alternatively, the data is consistent with the peak existing in both component mass distributions, a possibility not included in most previous studies. In either case, the peak is observed at 31.4 − 2.6 + 2.3 M ⊙ , which is shifted lower than the value obtained in previous analyses of the marginal primary mass distribution, placing this feature in further tension with expectations from a pulsational pair-instability supernova pileup.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".