4-OGC: Catalog of Gravitational Waves from Compact Binary Mergers
Bibliographic record
Abstract
Abstract We present the fourth Open Gravitational-wave Catalog (4-OGC) of binary neutron star (BNS), binary black hole (BBH), and neutron star–black hole (NSBH) mergers. The catalog includes observations from 2015 to 2020 covering the first through third observing runs (O1, O2, O3a, and O3b) of Advanced LIGO and Advanced Virgo. The updated catalog includes seven BBH mergers that were not previously reported with high significance during O3b for a total of 94 observations: 90 BBHs, 2 NSBHs, and 2 BNSs. The most confident new detection, GW200318_191337, has component masses 49.1 − 12.0 + 16.4 M ⊙ and 31.6 − 11.6 + 12.0 M ⊙ ; its redshift of 0.84 − 0.35 + 0.4 (90% credible interval) may make it the most distant merger so far. We estimate the merger rate of BBH sources, assuming a power-law mass distribution containing an additive Gaussian peak, to be 16.5 − 6.2 + 10.4 ( 25.0 − 8.0 + 12.6 ) Gpc−3 yr−1 at a redshift of z = 0 (0.2). For BNS and NSBH sources, we estimate a merger rate of 200 − 148 + 309 Gpc−3 yr−1 and 19 − 14 + 30 Gpc−3 yr−1, respectively, assuming the known sources are representative of the total population. We provide reference parameter estimates for each of these sources using an up-to-date model accounting for instrumental calibration uncertainty. The corresponding data release also includes our full set of subthreshold candidates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".