REVIEW 6 cited by
Performance of the low-latency GstLAL inspiral search towards LIGO, Virgo, and KAGRA's fourth observing run
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
GstLAL is a stream-based matched-filtering search pipeline aiming at the prompt discovery of gravitational waves from compact binary coalescences such as the mergers of black holes and neutron stars. Over the past three observation runs by the LIGO, Virgo, and KAGRA (LVK) collaboration, the GstLAL search pipeline has participated in several tens of gravitational wave discoveries. The fourth observing run (O4) is set to begin in May 2023 and is expected to see the discovery of many new and interesting gravitational wave signals which will inform our understanding of astrophysics and cosmology. We describe the current configuration of the GstLAL low-latency search and show its readiness for the upcoming observation run by presenting its performance on a mock data challenge. The mock data challenge includes 40 days of LIGO Hanford, LIGO Livingston, and Virgo strain data along with an injection campaign in order to fully characterize the performance of the search. We find an improved performance in terms of detection rate and significance estimation as compared to that observed in the O3 online analysis. The improvements are attributed to several incremental advances in the likelihood ratio ranking statistic computation and the method of background estimation.
Forward citations
Cited by 6 Pith papers
-
Detectability of Gravitational-wave counterparts of EP-FXTs observed during the O4b LIGO-Virgo-KAGRA Observing Run
No gravitational-wave counterpart is statistically associated with any of 47 Einstein Probe fast X-ray transients in the O4b run; 90% exclusion distances of ~178 Mpc (BNS) and ~349 Mpc (NSBH) disfavor nearby compact-b...
-
nmma: An extended Bayesian framework for Nuclear Multimessenger Astronomy in the Era of Next-Generation Detectors
nmma now jointly samples nuclear EoS parameters with GW and EM data via TOV emulators and Fiesta surrogates, delivering 20–60× speedups and future H0–nuclear constraints.
-
A neural network for estimating compact binary coalescence parameters of gravitational-wave events in real time
A quantile regression neural network produces real-time credible intervals for chirp mass, mass ratio, and total mass of compact binary mergers, with coverage mostly above 90%, and these intervals serve as priors that...
-
PINCH: Pipeline-Informed Noise Characterization in LIGO's Third Observing Run
PINCH uses support vector machines trained on clean GstLAL triggers to identify glitch-induced triggers, revealing class-specific patterns in how transient noise contaminates LIGO's third observing run.
-
A machine learning-enabled search for binary black hole mergers in LIGO-Virgo-KAGRAs third observing run
Aframe, a neural-network gravitational-wave search, recovers 38 previously known binary black hole mergers from O3 data and finds no new candidates, showing ML pipelines are viable but not yet superior to matched filtering.
-
All-sky search for short gravitational-wave bursts in the first part of the fourth LIGO-Virgo-KAGRA observing run
An all-sky search of O4a LIGO data finds no new gravitational-wave bursts and improves burst sensitivity and rate limits by factors of 2 to 10 over the previous run.
Discussion (0). Continue with ORCID to comment.