arXiv subjects

Vincent Boudart

Publications and source records attributed to Vincent Boudart.

4 recordsLinked to original sources

arXiv2022

Simulating Transient Noise Bursts in LIGO with gengli

In the field of gravitational-wave (GW) interferometers, the most severe limitation to the detection of transient signals from astrophysical sources comes from transient noise artefacts, known as glitches, that happens at a rate around $1$ per minute. Because glitches reduce the amount of scientific data available, there is a need for better modelling and inclusion of glitches in large-scale studies, such as stress testing the search pipelines and increasing the confidence of detection. In this work, we employ a Generative Adversarial Network (GAN) to produce a particular class of glitches ({\it blip}) in the time domain. We share the trained network through a user-friendly open-source software package called \texttt{gengli} and provide practical examples of its usage.

astro-ph.IM↗

arXiv2022

A convolutional neural network to distinguish glitches from minute-long gravitational wave transients

Gravitational wave bursts are transient signals distinct from compact binary mergers that arise from a wide variety of astrophysical phenomena. Because most of these phenomena are poorly modeled, the use of traditional search methods such as matched filtering is excluded. Bursts include short ($<$10 seconds) and long (from 10 to a few hundreds of seconds) duration signals for which the detection is constrained by environmental and instrumental transient noises called glitches. Glitches contaminate burst searches, reducing the amount of useful data and limiting the sensitivity of current algorithms. It is therefore of primordial importance to locate and distinguish them from potential burst signals. In this paper, we propose to train a convolutional neural network to detect glitches in the time-frequency space of the cross-correlated LIGO noise. We show that our network is retrieving more than 95$\%$ of the glitches while being trained only on a subset of the existing glitch classes highlighting the sensitivity of the network to completely new glitch classes.

gr-qc↗

arXiv2022

Simulating Transient Noise Bursts in LIGO with Generative Adversarial Networks

The noise of gravitational-wave (GW) interferometers limits their sensitivity and impacts the data quality, hindering the detection of GW signals from astrophysical sources. For transient searches, the most problematic are transient noise artifacts, known as glitches, that happen at a rate around $ 1 \text{ min}^{-1}$, and can mimic GW signals. Because of this, there is a need for better modeling and inclusion of glitches in large-scale studies, such as stress testing the pipelines. In this proof-of concept work we employ Generative Adversarial Networks (GAN), a state-of-the-art Deep Learning algorithm inspired by Game Theory, to learn the underlying distribution of blip glitches and to generate artificial populations. We reconstruct the glitch in the time-domain, providing a smooth input that the GAN can learn. With this methodology, we can create distributions of $\sim 10^{3}$ glitches from Hanford and Livingston detectors in less than one second. Furthermore, we employ several metrics to measure the performance of our methodology and the quality of its generations. This investigation will be extended in the future to different glitch classes with the final goal of creating an open-source interface for mock data generation.

astro-ph.IM↗

arXiv2022

A machine learning algorithm for minute-long Burst searches

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.

gr-qc↗