Large-Scale Structure of the Universe (eBook)
XII, 120 Seiten
Springer Nature Singapore (Verlag)
978-981-19-5880-9 (ISBN)
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.
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.
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.
| Erscheint lt. Verlag | 1.11.2022 |
|---|---|
| Reihe/Serie | Springer Theses | Springer Theses |
| Zusatzinfo | XII, 120 p. 46 illus., 44 illus. in color. |
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Naturwissenschaften ► Physik / Astronomie ► Astronomie / Astrophysik | |
| Naturwissenschaften ► Physik / Astronomie ► Relativitätstheorie | |
| Schlagworte | Convolutional Neural Netowrk • Cosmological Simulation • Emission Line Galaxy • Galaxy Formation and Evolution • Generative Adversarial Network • Large-Scale Structure of the Universe • Line Intensity Mapping • noise reduction • Signal reconstruction |
| ISBN-10 | 981-19-5880-7 / 9811958807 |
| ISBN-13 | 978-981-19-5880-9 / 9789811958809 |
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