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001450920 001__ 1450920
001450920 003__ OCoLC
001450920 005__ 20230310004550.0
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001450920 020__ $$a9789811958809$$q(electronic bk.)
001450920 020__ $$a9811958807$$q(electronic bk.)
001450920 020__ $$z9811958793
001450920 020__ $$z9789811958793
001450920 0247_ $$a10.1007/978-981-19-5880-9$$2doi
001450920 035__ $$aSP(OCoLC)1350183474
001450920 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dOCLCF$$dUKAHL
001450920 049__ $$aISEA
001450920 050_4 $$aQB991.L37
001450920 08204 $$a523.1$$223/eng/20221110
001450920 1001_ $$aMoriwaki, Kana,$$eauthor.
001450920 24510 $$aLarge-structure of the universe :$$bcosmological simulations and machine learning /$$cKana Moriwaki.
001450920 260__ $$aSingapore :$$bSpringer,$$c2022.
001450920 300__ $$a1 online resource
001450920 4901_ $$aSpringer theses,$$x2190-5061
001450920 500__ $$aDoctoral Thesis accepted by The University of Tokyo, Tokyo, Japan.
001450920 504__ $$aIncludes bibliographical references.
001450920 5050_ $$aIntroduction -- Observations of the Large-Scale Structure of the Universe -- Modeling Emission Line Galaxies -- Signal Extraction from Noisy LIM Data -- Signal Separation from Confused LIM Data -- Signal Extraction from 3D LIM Data -- Application of LIM Data for Studying Cosmic Reionization -- Summary and Outlook -- Appendix.
001450920 506__ $$aAccess limited to authorized users.
001450920 520__ $$aLine intensity mapping (LIM) is an observational technique that probes the large-scale structure of the Universe by collecting light from a wide field of the sky. This book demonstrates a novel analysis method for LIM using machine learning (ML) technologies. The author develops a conditional generative adversarial network that separates designated emission signals from sources at different epochs. It thus provides, for the first time, an efficient way to extract signals from LIM data with foreground noise. The method is complementary to conventional statistical methods such as cross-correlation analysis. When applied to three-dimensional LIM data with wavelength information, high reproducibility is achieved under realistic conditions. The book further investigates how the trained machine extracts the signals, and discusses the limitation of the ML methods. Lastly an application of the LIM data to a study of cosmic reionization is presented. This book benefits students and researchers who are interested in using machine learning to multi-dimensional data not only in astronomy but also in general applications.
001450920 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 10, 2022).
001450920 650_0 $$aLarge scale structure (Astronomy)
001450920 650_0 $$aAstronomy$$vObservations.
001450920 650_0 $$aMachine learning.
001450920 655_7 $$aObservations.$$2fast$$0(OCoLC)fst01423822
001450920 655_0 $$aElectronic books.
001450920 77608 $$iPrint version: $$z9811958793$$z9789811958793$$w(OCoLC)1336536283
001450920 830_0 $$aSpringer theses,$$x2190-5061
001450920 852__ $$bebk
001450920 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-5880-9$$zOnline Access$$91397441.1
001450920 909CO $$ooai:library.usi.edu:1450920$$pGLOBAL_SET
001450920 980__ $$aBIB
001450920 980__ $$aEBOOK
001450920 982__ $$aEbook
001450920 983__ $$aOnline
001450920 994__ $$a92$$bISE