Towards high-precision gravitational wave astronomy: robust and efficient data analysis for ground-based detectors

Hu, Qian (2024) Towards high-precision gravitational wave astronomy: robust and efficient data analysis for ground-based detectors. PhD thesis, University of Glasgow.

Abstract

Since the first direct detection of Gravitational Waves (GWs) by Laser Interferometer Gravitational-Wave Observatory (LIGO), GW astronomy has advanced rapidly across theoretical, observational, and instrumental sciences. While the detection rate from the LIGO-Virgo-KAGRA collaboration (LVK) continues to rise, several Third-Generation (3G) ground-based GW detectors are being proposed for the 2030s, aiming to detect millions of GW events per year with significantly improved signal-to-noise ratios. The increased precision in GW astronomy will generate vast amounts of data, posing challenges in data analysis concerning robustness and efficiency. Ensuring robustness in data analysis is crucial for deriving accurate scientific conclusions, while efficiency is essential for performing analyses within manageable timeframes and hardware constraints—especially for time-sensitive tasks in transient astronomy.

This thesis aims to investigate the challenges of robustness and efficiency and explore possible solutions. In Chapters 1 and 2, we give an overview of the basic concepts in GW astrophysics and data analysis, and bring up the robustness and efficiency challenges. For robustness, we show how data analysis can lead to incorrect scientific conclusions using the example of testing general relativity with inaccurate waveforms and overlapped signals in Chapter 3. We investigate the error accumulation effects on the catalog level and we identify the waveform inaccuracy as the primary contributor to systematic errors. Following this, in Chapter 4, we propose a waveform accuracy assessment approach that can be readily applied to parameter estimation results without numerical relativity simulations. With this method, we examine parameter estimation results from the latest LVK public event catalogs GWTC-2.1 and GWTC-3, and make predictions of waveform accuracy requirements for the future detectors. For efficiency, we focus on the long Binary Neutron Star (BNS) signals expected in the 3G detectors. In Chapter 5, we demonstrate pre-merger source localization for long BNS signals with multi-band matched filtering and a semi-analytical localization algorithm. Using our method, we show that it is possible to provide accurate sky localizations more than 30 minutes before the merger. We also provide a forecast on the detection rate of well-localized early-warning BNS events. Further, in Chapter 6, we develop machine learning models equipped with a suite of data preprocessing methods for the full parameter estimation of hours-long BNS signals, which is prohibitively slow using traditional methods. The models’ precision is validated against analytical forecasts and the accuracy is confirmed by self-consistency tests. The thesis concludes with a summary of the findings and an outlook on high-precision GW data analysis in Chapter 7.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: Supported by funding from the China Scholarship Council and Lord Kelvin/Charles Lindie Mitchell travel fund.
• Subjects: Q Science > QB Astronomy; Q Science > QC Physics
• Colleges/Schools: College of Science and Engineering > School of Physics and Astronomy
• Supervisor's Name: Veitch, Dr. John
• Date of Award: 2024
• Depositing User: Theses Team
• Unique ID: glathesis:2024-84751
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 14 Nov 2024 12:24
• Last Modified: 14 Nov 2024 12:25
• Thesis DOI: 10.5525/gla.thesis.84751
• URI: https://theses.gla.ac.uk/id/eprint/84751

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