Towards low-latency real-time detection of gravitational waves from compact binary coalescences in the era of advanced detectors

Abstract

Electromagnetic (EM) follow-up observations of gravitational wave (GW) events will help shed light on the nature of the sources, and more can be learned if the EM follow-ups can start as soon as the GW event becomes observable. In this paper, we propose a computationally efficient time-domain algorithm capable of detecting gravitational waves (GWs) from coalescing binaries of compact objects with nearly zero time delay. In case when the signal is strong enough, our algorithm also has the flexibility to trigger EM observation before the merger. The key to the efficiency of our algorithm arises from the use of chains of so-called Infinite Impulse Response (IIR) filters, which filter time-series data recursively. Computational cost is further reduced by a template interpolation technique that requires filtering to be done only for a much coarser template bank than otherwise required to sufficiently recover optimal signal-to-noise ratio. Towards future detectors with sensitivity extending to lower frequencies, our algorithm's computational cost is shown to increase rather insignificantly compared to the conventional time-domain correlation method. Moreover, at latencies of less than hundreds to thousands of seconds, this method is expected to be computationally more efficient than the straightforward frequency-domain method.

19 pages, 6 figures, for PRD

References in corpus (7)

• LIGO: The Laser Interferometer Gravitational-Wave Observatory
• Toward Early-Warning Detection of Gravitational Waves from Compact Binary Coalescence
• Singular value decomposition applied to compact binary coalescence gravitational-wave signals
• Reduced basis catalogs for gravitational wave templates
• LOOC UP: Locating and observing optical counterparts to gravitational wave bursts
• Efficiently enclosing the compact binary parameter space by singular-value decomposition
• Composite gravitational-wave detection of compact binary coalescence

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• GW190425: Observation of a Compact Binary Coalescence with Total Mass $\sim 3.4M_{\odot }$
• Observation of gravitational waves from two neutron star-black hole coalescences
• Summed Parallel Infinite Impulse Response (SPIIR) Filters For Low-Latency Gravitational Wave Detection
• First demonstration of early warning gravitational wave alerts
• Follow-up Analyses to the O3 LIGO-Virgo-KAGRA Lensing Searches
• Capturing the electromagnetic counterparts of binary neutron star mergers through low latency gravitational wave triggers
• Early warning of coalescing neutron-star and neutron-star-black-hole binaries from nonstationary noise background using neural networks
• Low-latency gravitational wave alert products and their performance at the time of the fourth LIGO-Virgo-KAGRA observing run
• Using negative-latency gravitational wave alerts to detect prompt radio bursts from binary neutron star mergers with the Murchison Widefield Array
• Early Warnings of Binary Neutron Star Coalescence using the SPIIR Search
• Ranking Candidate Signals with Machine Learning in Low-Latency Search for Gravitational-Waves from Compact Binary Mergers
• The SPIIR online coherent pipeline to search for gravitational waves from compact binary coalescences
• Gravitational wave astrophysics, data analysis and multimessenger astronomy
• Hunting Gravitational Waves with Multi-Messenger Counterparts: Australia's Role
• Gravitational-wave searches in the era of Advanced LIGO and Virgo
• Detection of GW200105 with a targeted eccentric search
• Gravitational lensing aided luminosity distance estimation for compact binary coalescences
• What to expect: kilonova light curve predictions via equation of state marginalization
• Astrophysical or Terrestrial: Machine learning classification of gravitational-wave candidates using multiple-search information
• Enhancing gravitational-wave detection: a machine learning pipeline combination approach with robust uncertainty quantification