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Simulating transient burst noise with gengli

Published online by Cambridge University Press:  01 August 2025

Melissa Lopez Open the ORCID record for Melissa Lopez [Opens in a new window] ,
Vincent Boudart ,
Stefano Schmidt  and
Sarah Caudill
Melissa Lopez*
Affiliation:
Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University, Princetonplein 1, 3584 CC Utrecht, The Netherlands Nikhef, Science Park 105, 1098 XG, Amsterdam, The Netherlands
Vincent Boudart
Affiliation:
STAR Institute, Bâtiment B5, Université de Liège, Sart Tilman B4000 Liège, Belgium
Stefano Schmidt
Affiliation:
Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University, Princetonplein 1, 3584 CC Utrecht, The Netherlands Nikhef, Science Park 105, 1098 XG, Amsterdam, The Netherlands
Sarah Caudill
Affiliation:
Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University, Princetonplein 1, 3584 CC Utrecht, The Netherlands Department of Physics, University of Massachusetts, Dartmouth, MA 02747, USA Center for Scientific Computing and Visualization Research, University of Massachusetts, Dartmouth, MA 02747, USA
*
email: m.lopez@uu.nl
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Abstract

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 (blip) in the time domain. We share the trained network through a user-friendly open-source software package called <monospace>gengli</monospace> and provide practical examples of its usage.

Keywords

Generative adversarial networksgravitational wavessynthetic datamachine learning

Information

Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S368: Machine Learning in Astronomy: Possibilities and Pitfalls , August 2023 , pp. 73 - 79
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

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