Overview
- Nominated as an outstanding Ph. D. thesis by The University of Tokyo
- Offers a novel technique based on machine learning for analysis of astronomical observational data
- Develops conditional generative adversarial networks using physical information in data
Part of the book series: Springer Theses (Springer Theses)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Line intensity mapping (LIM) is an observational technique that probes the large-scale structure of the Universe by collecting light from a wide field of the sky. This book demonstrates a novel analysis method for LIM using machine learning (ML) technologies. The author develops a conditional generative adversarial network that separates designated emission signals from sources at different epochs. It thus provides, for the first time, an efficient way to extract signals from LIM data with foreground noise. The method is complementary to conventional statistical methods such as cross-correlation analysis. When applied to three-dimensional LIM data with wavelength information, high reproducibility is achieved under realistic conditions. The book further investigates how the trained machine extracts the signals, and discusses the limitation of the ML methods. Lastly an application of the LIM data to a study of cosmic reionization is presented. This book benefits students and researchers who are interested in using machine learning to multi-dimensional data not only in astronomy but also in general applications.
Authors and Affiliations
About the author
Kana Moriwaki is an assistant professor in the School of Science at the University of Tokyo. She received her Ph.D. from the University of Tokyo in 2022 and was awarded the University of Tokyo President's Grand Prize. Her interest lies in cosmological simulations and the application of machine learning techniques for astronomical data.
Bibliographic Information
Book Title: Large-Scale Structure of the Universe
Book Subtitle: Cosmological Simulations and Machine Learning
Authors: Kana Moriwaki
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-981-19-5880-9
Publisher: Springer Singapore
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
Hardcover ISBN: 978-981-19-5879-3Published: 02 November 2022
Softcover ISBN: 978-981-19-5882-3Published: 03 November 2023
eBook ISBN: 978-981-19-5880-9Published: 01 November 2022
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XII, 120
Number of Illustrations: 2 b/w illustrations, 44 illustrations in colour
Topics: Cosmology, Machine Learning, Astrophysics and Astroparticles, Astronomy, Observations and Techniques