Large-Scale Structure of the Universe: Cosmological Simulations and Machine Learning

Line intensity mapping (LIM) is an observational technique that analyzes the large-scale structure of the Universe by collecting light from a wide field of the sky. This book presents a novel analysis method for LIM using machine learning (ML) technologies, which efficiently extracts signals from LIM data with foreground noise. The method is complementary to conventional statistical methods and can be applied to three-dimensional LIM data with wavelength information. An application of the LIM data to a study of cosmic reionization is also presented. This book benefits students and researchers interested in using ML to multi-dimensional data in astronomy and other fields.

Format: Hardback
Length: 120 pages
Publication date: 02 November 2022
Publisher: Springer Verlag, Singapore

Line intensity mapping (LIM) is a groundbreaking observational technique that delves into the intricate structure of the Universe by capturing light from a vast expanse of the celestial canvas. In this captivating book, the author presents a novel approach to analyzing LIM data through the powerful realm of machine learning (ML) technologies. By employing a conditional generative adversarial network, the author skillfully separates designated emission signals from their sources across different epochs. This groundbreaking method not only offers an efficient means to extract signals from LIM data plagued by foreground noise but also complements conventional statistical methods like cross-correlation analysis.

When applied to three-dimensional LIM data that includes wavelength information, remarkable reproducibility is achieved under realistic conditions. The book takes a deeper dive into the inner workings of the trained machine, shedding light on how it extracts the valuable signals from the complex data. Furthermore, it explores the limitations of ML methods and presents an intriguing application of LIM data to the study of cosmic reionization.

This invaluable resource is a treasure trove for students and researchers alike, who are eager to leverage the power of ML to unravel the secrets of multi-dimensional data in astronomy and beyond. Whether you are an astrophysicist, data scientist, or simply passionate about exploring the vastness of the Universe, this book provides a comprehensive and insightful journey into the realm of LIM analysis and its potential applications. So, embark on this captivating exploration and unlock the hidden treasures of the cosmos through the lens of machine learning.

Weight: 371g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811958793
Edition number: 1st ed. 2022