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

• Record Nr.: UNISA996499865403316
• Autore: Moriwaki Kana
• Pubbl/distr/stampa: Singapore : , : Springer, , [2022]; ©2022
• ISBN: 9789811958809; 9789811958793
• Descrizione fisica: 1 online resource (126 pages)
• Collana: Springer theses
• Disciplina: 520
• Soggetti: Astronomy; Large scale structure (Astronomy)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Supervisor's Foreword -- Acknowledgments -- Contents -- 1 Introduction -- References -- 2 Observations of the Large-Scale Structure of the Universe -- 2.1 Large-Scale Structure of the Universe -- 2.2 Observations of Large-Scale Distribution of the Galaxies -- 2.2.1 Galaxy Surveys -- 2.2.2 Line Intensity Mapping -- 2.3 Observations of the Cosmic Reionization -- 2.3.1 Current Observational Constraints on the Reionization -- 2.3.2 Observations of the 21-cm Lines at the EoR -- References -- 3 Modeling Emission Line Galaxies -- 3.1 Line Emissions from Hii Regions -- 3.2 Emission Line Model -- 3.3 Mock Observational Line Intensity Maps -- References -- 4 Signal Extraction from Noisy LIM Data -- 4.1 Machine Learning Algorithms -- 4.1.1 Basics of Neural Networks -- 4.1.2 Convolutional Neural Networks -- 4.1.3 Generative Adversarial Networks -- 4.2 Methods: Training Data and Network Architecture -- 4.3 Extracted Signals from Noisy Maps -- 4.4 Discussions -- 4.4.1 Different Emission Line Models -- 4.4.2 Choice of Training Data -- 4.5 Conclusion -- References -- 5 Signal Separation from Confused LIM Data -- 5.1 Line Confusion Problem in Line Intensity Mapping Observations -- 5.2 Methods: One-to-Many Translation Network Architecture -- 5.3 Separation of Multiple Emission Line Signals -- 5.4 Discussions -- 5.4.1 Different Emission Line Models -- 5.4.2 Combining Multiple ; Networks -- 5.4.3 Convolutional Filters and Hidden Layers -- 5.5 Conclusion -- References -- 6 Signal Extraction from 3D LIM Data -- 6.1 Methods -- 6.1.1 Data Preparation -- 6.1.2 Physics-Informed Network Architecture -- 6.2 Reconstruction of Three-Dimensional Large-Scale Structures -- 6.3 Understanding the Networks -- 6.4 Conclusion -- References -- 7 Application of LIM Data for Studying Cosmic Reionization -- 7.1 Methods -- 7.1.1 Reionization Simulation -- 7.1.2 [Oiii] Line Emission.; 7.2 Cross-Power Spectra -- 7.3 Discussions -- 7.3.1 Small-Scale Signals -- 7.3.2 Large-Scale Signals -- 7.3.3 Detectability of the Signals -- 7.4 Conclusion -- References -- 8 Summary and Outlook -- References -- Appendix A Training of the Generative Models -- A.1 Loss Functions of GANs -- A.2 Choice of Training Models and Datasets -- Appendix B 21-cm Line from Intergalactic Medium -- B.1 Brightness Temperature -- B.2 Noise Power Spectrum.