Gravitational Wave Science with Machine Learning
Series: Springer Series in Astrophysics and Cosmology;
- Publisher's listprice EUR 139.09
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59 001 Ft (56 192 Ft + 5% VAT)
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59 001 Ft
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Product details:
- Publisher Springer
- Date of Publication 12 April 2025
- Number of Volumes 1 pieces, Book
- ISBN 9789819617364
- Binding Hardback
- No. of pages289 pages
- Size 235x155 mm
- Language English
- Illustrations 4 Illustrations, black & white; 104 Illustrations, color 700
Categories
Short description:
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.
MoreLong description:
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.
MoreTable of Contents:
1. Neural network time-series classifiers for gravitational-wave searches in single-detector periods.- 2. A simple self similarity-based unsupervised noise monitor for gravitational-wave detectors.- 3 Simulation of transient noise bursts in gravitational wave interferometers.- 4. Efficient ML Algorithms for Detecting Glitches and Data Patterns in LIGO Time Series.- 5. Denoising gravitational-wave signals from binary black holes with dilated convolutional autoencoder.
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