A machine learning algorithm for minute-long Burst searches

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 pixel-wise detection despite trained with minimal assumptions, while being able to retrieve both astrophysical signals and noise transients originating from instrumental coupling within the detectors. We also note that our neural network can extrapolate and connect partially disjoint signal tracks in the time-frequency plane.