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1. |
Record Nr. |
UNINA9910254614503321 |
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Autore |
Wang Yan |
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Titolo |
First-stage LISA Data Processing and Gravitational Wave Data Analysis : Ultraprecise Inter-satellite Laser Ranging, Clock Synchronization and Novel Gravitational Wave Data Analysis Algorithms / / by Yan Wang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (252 p.) |
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Collana |
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Springer Theses, Recognizing Outstanding Ph.D. Research, , 2190-5053 |
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Disciplina |
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Soggetti |
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Astrophysics |
Lasers |
Photonics |
Data mining |
Gravitation |
Physical measurements |
Measurement |
Quantum optics |
Astrophysics and Astroparticles |
Optics, Lasers, Photonics, Optical Devices |
Data Mining and Knowledge Discovery |
Classical and Quantum Gravitation, Relativity Theory |
Measurement Science and Instrumentation |
Quantum Optics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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"Doctoral Thesis accepted by Max Planck Institute for Gravitational Physics, Germany." |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Introduction -- LISA data processing chain -- Applying the Kalman filter to a simple case -- The inter-spacecraft measurements -- Design a hybrid extended Kalman filter for the entire LISA constellation -- Alternative Kalman filter models -- Broken laser links and robustness -- Optimal filtering for LISA with effective system models -- Clock |
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noise and disordered measurements -- Octahedron configuration for a displacement noise-canceling gravitational wave detector in space -- EMRI data analysis with a phenomenological waveform -- Fast detection and automatic parameter estimation of a gravitational wave signal with a novel method -- Likelihood transform: making optimization and parameter estimation easier. . |
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Sommario/riassunto |
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This thesis covers a diverse set of topics related to space-based gravitational wave detectors such as the Laser Interferometer Space Antenna (LISA). The core of the thesis is devoted to the preprocessing of the interferometric link data for a LISA constellation, specifically developing optimal Kalman filters to reduce arm length noise due to clock noise. The approach is to apply Kalman filters of increasing complexity to make optimal estimates of relevant quantities such as constellation arm length, relative clock drift, and Doppler frequencies based on the available measurement data. Depending on the complexity of the filter and the simulated data, these Kalman filter estimates can provide up to a few orders of magnitude improvement over simpler estimators. While the basic concept of the LISA measurement (Time Delay Interferometry) was worked out some time ago, this work brings a level of rigor to the processing of the constellation-level data products. The thesis concludes with some topics related to the eLISA such as a new class of phenomenological waveforms for extreme mass-ratio inspiral sources (EMRIs, one of the main source for eLISA), an octahedral space-based GW detector that does not require drag-free test masses, and some efficient template-search algorithms for the case of relatively high SNR signals. |
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2. |
Record Nr. |
UNINA9910349271503321 |
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Titolo |
Discovery Science : 22nd International Conference, DS 2019, Split, Croatia, October 28–30, 2019, Proceedings / / edited by Petra Kralj Novak, Tomislav Šmuc, Sašo Džeroski |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (xxii, 546 pages) : illustrations |
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Collana |
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Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 11828 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Data mining |
Computer science |
Image processing - Digital techniques |
Computer vision |
Software engineering |
Artificial Intelligence |
Data Mining and Knowledge Discovery |
Theory of Computation |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Software Engineering |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Advanced Machine Learning -- Applications -- Data and Knowledge Representation -- Feature Importance -- Interpretable Machine Learning -- Networks -- Pattern Discovery -- Time Series. |
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Sommario/riassunto |
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This book constitutes the proceedings of the 22nd International Conference on Discovery Science, DS 2019, held in Split, Coratia, in October 2019. The 21 full and 19 short papers presented together with 3 abstracts of invited talks in this volume were carefully reviewed and selected from 63 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific |
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knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Advanced Machine Learning; Applications; Data and Knowledge Representation; Feature Importance; Interpretable Machine Learning; Networks; Pattern Discovery; and Time Series. |
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