A machine learning algorithm for minute-long Burst searches
Vincent Boudart, Maxime Fays

TL;DR
This paper introduces a CNN-based anomaly detection method for minute-long gravitational wave transients, enabling rapid, template-free searches for diverse astrophysical phenomena with minimal assumptions.
Contribution
It presents a novel application of CNNs for anomaly detection in long-duration GW signals, capable of identifying signals and noise transients without detailed models.
Findings
Achieves pixel-wise detection of GW signals and noise transients.
Can extrapolate and connect disjoint signal tracks in time-frequency space.
Operates effectively with minimal prior assumptions.
Abstract
Minute-long Gravitational Wave (GW) transients are events lasting from few to hundreds of seconds. In opposition to compact binary mergers, their GW signals cover a wide range of poorly understood astrophysical phenomena such as accretion disk instabilities and magnetar flares. The lack of accurate and rapidly generated gravitational-wave emission models prevents the use of matched filtering methods. Such events are thus probed through the template-free excess-power method, consisting in searching for a local excess of power in the time-frequency space correlated between detectors. The problem can be viewed as a search for high-value clustered pixels within an image, which has been generally tackled by deep learning algorithms such as Convolutional Neural Networks (CNNs). In this work, we use a CNN as a anomaly detection tool for the long-duration searches. We show that it can reach a…
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