Master Thesis Online
Enhancement of a machine learning algorithm for long-duration gravitational wave burst searches
Peters, Sacha 
Promotor(s) :
Fays, Maxime
Date of defense : 27-Jun-2024/28-Jun-2024 • Permalink : http://hdl.handle.net/2268.2/20153
master_thesis_peters_sacha.pdfDetails
| Title : | Enhancement of a machine learning algorithm for long-duration gravitational wave burst searches |
| Author : | Peters, Sacha
|
| Date of defense : | 27-Jun-2024/28-Jun-2024 |
| Advisor(s) : | Fays, Maxime
|
| Committee's member(s) : | Cudell, Jean-René
Louppe, Gilles
Pracchia, Matteo
|
| Language : | English |
| Discipline(s) : | Physical, chemical, mathematical & earth Sciences > Space science, astronomy & astrophysics |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Master en sciences spatiales, à finalité approfondie |
| Faculty: | Master thesis of the Faculté des Sciences |
Abstract
[en] Minute-long gravitational wave burst signals are searched for in spectrograms of data from laser interferometers. ALBUS is a convolutional neural network that highlights potential signals in these spectrograms. This work focuses on improving the clustering of the triggers identified by ALBUS.
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The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.