Are Long Gamma-Ray Bursts Progenitors to Merging Binary Black Holes?
Bibliographic record
Abstract
Abstract The distribution of delay times between the formation of binary black hole (BBH) progenitors and their gravitational-wave (GW) merger provides important clues about their unknown formation histories. When inferring the delay time distribution, it is typically assumed that BBH progenitor formation traces the star formation rate (SFR). In this work, we consider the rate of long gamma-ray bursts (LGRBs) instead of the SFR. LGRBs are thought to correspond to the formation of (possibly spinning) black holes, and may therefore be related to the BBH progenitor population. By comparing the redshift evolution of the LGRB rate as inferred by G. Ghirlanda & R. Salvaterra and the BBH merger rate inferred by LIGO-Virgo-KAGRA observations, we find that the delay time distribution between LGRBs and BBH mergers is well described by a power law with minimum delay time 10 Myr and slope α = − 0.96 − 0.76 + 0.64 (90% credibility). This matches theoretical expectations for the BBH delay time distribution, which in turn lends support to the hypothesis that LGRBs trace BBH progenitor formation. However, comparing the absolute rates of these two populations, we find that at most f = 4 − 2 + 10 % of LGRBs may evolve into merging BBHs. We also consider the possibility that LGRBs only produce BBH systems with large aligned spins (with effective inspiral spin χ eff > 0.2). In this case, we find f = 0 . 3 − 0.2 + 1.0 % and the delay time distribution favors the steepest power-law slopes we consider (α = −2). We argue that asynchronous observations of LGRBs and GWs provide a powerful multimessenger probe of black hole life cycles across cosmic history.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".