Large-structure of the universe : cosmological simulations and machine learning / Kana Moriwaki.
Moriwaki, Kana, author.
2022
QB991.L37
Available Online

Linked e-resources

Linked Resource Online Access
Concurrent users Unlimited
Authorized users Authorized users
Document Delivery Supplied Can lend chapters, not whole ebooks

Details

Title Large-structure of the universe : cosmological simulations and machine learning / Kana Moriwaki.
Author Moriwaki, Kana, author.
ISBN 9789811958809 (electronic bk.)
9811958807 (electronic bk.)
9811958793
9789811958793
DOI https://doi.org/10.1007/978-981-19-5880-9
Publication Details Singapore : Springer, 2022.
Language English
Description 1 online resource
Item Number 10.1007/978-981-19-5880-9 doi
Call Number QB991.L37
Dewey Decimal Classification 523.1
Summary 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.
Note Doctoral Thesis accepted by The University of Tokyo, Tokyo, Japan.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed November 10, 2022).
Series Springer theses, 2190-5061
Available in Other Form Print version: 9789811958793
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Introduction
Observations of the Large-Scale Structure of the Universe
Modeling Emission Line Galaxies
Signal Extraction from Noisy LIM Data
Signal Separation from Confused LIM Data
Signal Extraction from 3D LIM Data
Application of LIM Data for Studying Cosmic Reionization
Summary and Outlook
Appendix.

Browse Subjects

Show more subjects...

Statistics

from
to
Export