[no title]
Bibliographic record
Abstract
We present results on the mass, spin, and redshift distributions of the ten binary black hole mergers detected in the first and second observing runs completed by Advanced LIGO and Advanced Virgo. We constrain properties of the binary black hole (BBH) mass spectrum using models with a range of parameterizations of the BBH mass and spin distributions. We find that the mass distribution of the more massive black hole in such binaries is well approximated by models with no more than 1% of black holes more massive than $45 M{}_{\\odot}$, and a power law index of $\\alpha = {{1.6}^{+1.5}_{-1.7}}$ (90% credibility). We also show that BBHs are unlikely to be composed of black holes with large spins aligned to the orbital angular momentum. Modelling the evolution of the BBH merger rate with redshift, we show that it is flat or increasing with redshift with $88$% probability. Marginalizing over uncertainties in the BBH population, we find robust estimates of the BBH merger rate density of $R = {{53.2}^{+58.5}_{-28.8}}$ Gpc$^{-3}$ yr$^{-1}$ (90% credibility). As the BBH catalog grows in future observing runs, we expect that uncertainties in the population model parameters will shrink, potentially providing insights into the formation of black holes via supernovae, binary interactions of massive stars, stellar cluster dynamics, and the formation history of black holes across cosmic time.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.017 |
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".