LIGO–Virgo–KAGRA's Oldest Black Holes: Probing Star Formation at Cosmic Noon With GWTC-3
Bibliographic record
Abstract
Abstract In their third observing run, the LIGO–Virgo–KAGRA gravitational-wave (GW) observatory was sensitive to binary black hole (BBH) mergers out to redshifts z merge ≈ 1. Because GWs are inefficient at shrinking the binary orbit, some of these BBH systems likely experienced long delay times τ between the formation of their progenitor stars at z form and their GW merger at z merge. In fact, the distribution of delay times predicted by isolated binary evolution resembles a power law p ( τ ) ∝ τ α τ with slope −1 ≲ α τ ≲ −0.35 and a minimum delay time of τ min = 10 Myr . We use these predicted delay time distributions to infer the formation redshifts of the ∼70 BBH events reported in the third GW transient catalog GWTC-3 and the formation rate of BBH progenitors. For our default α τ = –1 delay time distribution, we find that GWTC-3 contains at least one system (with 90% credibility) that formed earlier than z form > 4.4. Comparing our inferred BBH progenitor formation rate to the star formation rate, we find that at z form = 4, the number of BBH progenitor systems formed per stellar mass was 6.4 − 5.5 + 9.4 × 10 − 6 M ⊙ − 1 and this yield dropped to 0.134 − 0.127 + 1.6 × 10 − 6 M ⊙ − 1 by z form = 0. We discuss implications of this measurement for the cosmic metallicity evolution, finding that for typical assumptions about the metallicity dependence of the BBH yield, the average metallicity at z form = 4 was 〈 log 10 ( Z / Z ⊙ ) 〉 = − 0.3 − 0.4 + 0.3
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".