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Rapid Bayesian position reconstruction for gravitational-wave transients

6 Pith papers cite this work. Polarity classification is still indexing.

6 Pith papers citing it
abstract

Within the next few years, Advanced LIGO and Virgo should detect gravitational waves from binary neutron star and neutron star-black hole mergers. These sources are also predicted to power a broad array of electromagnetic transients. Because the electromagnetic signatures can be faint and fade rapidly, observing them hinges on rapidly inferring the sky location from the gravitational-wave observations. Markov chain Monte Carlo methods for gravitational-wave parameter estimation can take hours or more. We introduce BAYESTAR, a rapid, Bayesian, non-Markov chain Monte Carlo sky localization algorithm that takes just seconds to produce probability sky maps that are comparable in accuracy to the full analysis. Prompt localizations from BAYESTAR will make it possible to search electromagnetic counterparts of compact binary mergers.

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gr-qc 6

years

2026 5 2021 1

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GstLAL O4 Online Results Paper

gr-qc · 2026-05-18 · unverdicted · novelty 3.0

GstLAL produced low-latency alerts for 250 astrophysically plausible gravitational-wave candidates during O4, providing the first upload for 222 and the sole upload for 75, with 88 percent of significant catalog events detected and 93 percent classification agreement.

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