Neural network time-series classifiers for gravitational-wave searches in single-detector periods
A. Trovato, \'E. Chassande-Mottin, M. Bejger, R. Flamary, N.Courty

TL;DR
This paper investigates neural network classifiers, including CNN, TCN, and Inception Time, to detect gravitational-wave signals in single-detector data, demonstrating their potential to identify black hole mergers without multi-detector coincidence.
Contribution
It introduces and evaluates neural network architectures specifically designed for time-series data to improve gravitational-wave detection in single-detector periods.
Findings
Neural network classifiers can identify potential black hole merger signals in single-detector data.
The most promising candidate aligns with a binary black hole merger at specific mass and distance parameters.
Classifiers show promise but do not yet achieve the significance levels of traditional multi-detector methods.
Abstract
The search for gravitational-wave signals is limited by non-Gaussian transient noises that mimic astrophysical signals. Temporal coincidence between two or more detectors is used to mitigate contamination by these instrumental glitches. However, when a single detector is in operation, coincidence is impossible, and other strategies have to be used. We explore the possibility of using neural network classifiers and present the results obtained with three types of architectures: convolutional neural network, temporal convolutional network, and inception time. The last two architectures are specifically designed to process time-series data. The classifiers are trained on a month of data from the LIGO Livingston detector during the first observing run (O1) to identify data segments that include the signature of a binary black hole merger. Their performances are assessed and compared. We…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Seismology and Earthquake Studies · Astronomical Observations and Instrumentation
