Low-latency gravitational wave alert products and their performance at the time of the fourth LIGO-Virgo-KAGRA observing run
Bibliographic record
Abstract
Multimessenger searches for binary neutron star (BNS) and neutron star-black hole (NSBH) mergers are currently one of the most exciting areas of astronomy. The search for joint electromagnetic and neutrino counterparts to gravitational wave (GW)s has resumed with ALIGO's, AdVirgo's and KAGRA's fourth observing run (O4). To support this effort, public semiautomated data products are sent in near real-time and include localization and source properties to guide complementary observations. In preparation for O4, we have conducted a study using a simulated population of compact binaries and a mock data challenge (MDC) in the form of a real-time replay to optimize and profile the software infrastructure and scientific deliverables. End-toend performance was tested, including data ingestion, running online search pipelines, performing annotations, and issuing alerts to the astrophysics community. We present an overview of the low-latency infrastructure and the performance of the data products that are now being released during O4 based on the MDC. We report the expected median latency for the preliminary alert of full bandwidth searches (29.5 s) and show consistency and accuracy of released data products using the MDC. We report the expected median latency for triggers from early warning searches (-3.1 s), which are new in O4 and target neutron star mergers during inspiral phase. This paper provides a performance overview for LIGO-Virgo-KAGRA (LVK) low-latency alert infrastructure and data products using theMDCand serves as a useful reference for the interpretation of O4 detections.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".