Gravitational Wave Science with Machine Learning

Elena Cuoco

ISBN 10: 9819617367 / ISBN 13: 9789819617364

Published by Springer, 2025

About this Item

Gravitational Wave Science with Machine Learning | Elena Cuoco | Buch | Springer Series in Astrophysics and Cosmology | xxv | Englisch | 2025 | Springer | EAN 9789819617364 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Seller Inventory # 132471809

Bibliographic Details

• Title: Gravitational Wave Science with Machine Learning
• Publisher: Springer
• Publication Date: 2025
• Language: English
• ISBN 10: 9819617367
• ISBN 13: 9789819617364
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 22D millimeters 241 millimeters width by 160 millimeters height by 22 millimeters depth
• Weight: 696 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This book highlights the state of the art of machine learning applied to the science of gravitational waves. The main topics of the book range from the search for astrophysical gravitational wave signals to noise suppression techniques and control systems using machine learning-based algorithms. During the four years of work in the COST Action CA17137-A network for Gravitational Waves, Geophysics and Machine Learning (G2net), the collaboration produced several original publications as well as tutorials and lectures in the training schools we organized. The book encapsulates the immense amount of finding and achievements.

It is a timely reference for young researchers approaching the analysis of data from gravitational wave experiments, with alternative approaches based on the use of artificial intelligence techniques.

About the Author

Dr. Elena Cuoco is Full Professor at the University of Bologna since 2024, she conducts research in the field of gravitational waves. She is a member of the LIGO/Virgo/KAGRA collaboration, where she works on data analysis and the application of artificial intelligence techniques for detector characterization and the search for gravitational signals of astrophysical origin. From 2018 to 2023, she served as the Action Chair for COST Action CA17137, dedicated to the application of machine learning to gravitational wave science. Author of numerous scientific publications, she is involved in various initiatives at the European and international levels.