Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA
Bibliographic record
Abstract
Matched-filtering gravitational-wave search pipelines identify gravitational-wave signals by computing correlations, i.e., signal-to-noise ratios, between gravitational-wave detector data and gravitational-wave template waveforms. Intrinsic parameters, the component masses and spins, of the gravitational-wave waveforms are often stored in ``template banks,'' and the construction of a densely populated template bank is essential for some gravitational-wave search pipelines. This paper presents a template bank that is currently being used by the GstLAL-based compact binary search pipeline in the fourth observing run of the LIGO, Virgo, and KAGRA collaboration, and was generated with a new binary tree approach of placing templates, manifold. The template bank contains $1.8\ifmmode\times\else\texttimes\fi{}{10}^{6}$ sets of template parameters covering plausible neutron star and black hole systems up to a total mass of $400{M}_{\ensuremath{\bigodot}}$ with component masses between $1--200{M}_{\ensuremath{\bigodot}}$ and mass ratios between 1 and 20 under the assumption that each component object's angular momentum is aligned with the orbital angular momentum. We validate the template bank generated with our new method, manifold, by comparing it with a template bank generated with the previously used stochastic template placement method. We show that both template banks have similar effectualness. The GstLAL search pipeline performs singular value decomposition (SVD) on the template banks to reduce the number of filters used. We describe a new grouping of waveforms that improves the computational efficiency of SVD by nearly 5 times as compared to previously reported SVD sorting schemes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".