Large-structure of the universe : cosmological simulations and machine learning / Kana Moriwaki.

Format:

Book

Author/Creator:

Moriwaki, Kana, author.

Series:

Springer theses 2190-5061

Springer theses, 2190-5061

Language:

English

Subjects (All):

Large scale structure (Astronomy).

Astronomy--Observations.

Astronomy.

Machine learning.

Genre:

Observations.

Physical Description:

1 online resource

Place of Publication:

Singapore : Springer, 2022.

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.

Contents:

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.

Notes:

Doctoral Thesis accepted by The University of Tokyo, Tokyo, Japan.

Includes bibliographical references.

Online resource; title from PDF title page (SpringerLink, viewed November 10, 2022).

ISBN:

9789811958809

9811958807

OCLC:

1350183474

Access Restriction:

Restricted for use by site license.