3-OGC: Catalog of Gravitational Waves from Compact-binary Mergers
Bibliographic record
Abstract
Abstract We present the third open gravitational-wave catalog (3-OGC) of compact-binary coalescences, based on the analysis of the public LIGO and Virgo data from 2015 through 2019 (O1, O2, O3a). Our updated catalog includes a population of 57 observations, including 4 binary black hole mergers that had not been previously reported. This consists of 55 binary black hole mergers and the 2 binary neutron star mergers, GW170817 and GW190425. We find no additional significant binary neutron star or neutron star–black hole merger events. The most confident new detection is the binary black hole merger GW190925_232845, which was observed by the LIGO–Hanford and Virgo observatories with astro > 0.99 ; its primary and secondary component masses are 20.2 − 2.5 + 3.9 M ⊙ and 15.6 − 2.6 + 2.1 M ⊙ , respectively. We estimate the parameters of all binary black hole events using an up-to-date waveform model that includes both subdominant harmonics and precession effects. To enable deep follow up as our understanding of the underlying populations evolves, we make available our comprehensive catalog of events, including the subthreshold population of candidates, and the posterior samples of our source parameter estimates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.014 |
| 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) | 0.008 | 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".