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Searching for Gravitational Waves from Compact Binary Coalescences and Stochastic Backgrounds in the LIGO–Virgo Detector Network

Citation

Xiao, Liting (2023) Searching for Gravitational Waves from Compact Binary Coalescences and Stochastic Backgrounds in the LIGO–Virgo Detector Network. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/zgtx-0582. https://resolver.caltech.edu/CaltechTHESIS:10202022-200320341

Abstract

Gravitational waves (GWs) are ripples in spacetime generated by accelerating masses, carrying away information about the underlying processes. There are four main astrophysical sources detectable in the sensitive band of the LIGO–VIRGO– KAGRA (LVK) GW detector network: compact binary coalescences, burst sources, continuous waves and stochastic gravitational-wave backgrounds. This thesis focuses on the detection methods of two of these categories, coalescing compact binaries and stochastic backgrounds, and their search results across LIGO–Virgo’s first three observing runs spanning from 2015 to 2020. Compact binary coalescences of black holes and/or neutron stars are the only type of GW sources detected so far in the LVK frequency band. Such binary systems lose orbital energy via GW emission and are compact enough to merge within the age of the Universe. PyCBC is a matched-filter, all-sky pipeline for GW signals from compact binary mergers using a bank of modeled gravitational waveform templates. We describe the methods employed in PyCBC and present the developmental updates both in its archival and low-latency configurations for LIGO–Virgo’s third observing run. Using PyCBC to analyze the data from LIGO–Virgo’s first three observing runs, we summarize our results of the searches in gravitational-wave transient catalogs and characterize some exceptional events. A stochastic gravitational-wave background consists of a large number of weak, independent and uncorrelated events of astrophysical or cosmological origin. The GW power on the sky is assumed to contain anisotropic components on top of an isotropic angular monopole. Complimentary to the LVK searches, we develop an efficient analysis pipeline to compute the maximum-likelihood anisotropic sky maps in stochastic backgrounds directly in the sky pixel domain using data folded over one sidereal day. We invert the "full" pixel-pixel correlation matrix in map-making of the GW sky, up to an optimal eigenmode cutoff decided systematically using simulations. In addition to modeled mapping, we implement a model-independent method to probe spectral shapes of stochastic backgrounds. Using data from LIGO–Virgo’s first three observing runs, we obtain upper limits on anisotropies as well as the isotropic monopole as a limiting case, consistent with the LVK results. We also set constraints on the spectral shape of the stochastic background using the novel model-independent method.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Gravitational waves; LIGO; compact binary coalescences; stochastic backgrounds;
Degree Grantor:California Institute of Technology
Division:Physics, Mathematics and Astronomy
Major Option:Physics
Thesis Availability:Not set
Research Advisor(s):
  • Weinstein, Alan Jay
Thesis Committee:
  • Chen, Yanbei (chair)
  • Weinstein, Alan Jay
  • Reitze, David H.
  • Bouman, Katherine L.
Defense Date:13 October 2022
Non-Caltech Author Email:naomixiao824 (AT) gmail.com
Record Number:CaltechTHESIS:10202022-200320341
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:10202022-200320341
DOI:10.7907/zgtx-0582
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/1538-4357/ac2f9aDOIAdapted for Chapter 4
https://doi.org/10.1103/physrevx.9.031040DOIAdapted for Chapter 5
https://doi.org/10.1103/physrevx.11.021053DOIAdapted for Chapter 5
https://arxiv.org/abs/2108.01045arXivAdapted for Chapter 5
https://arxiv.org/abs/2111.03606arXivAdapted for Chapter 5
https://doi.org/10.3847/2041-8213/ab75f5DOIAdapted for Chapter 5
https://dcc.ligo.org/LIGO-T1800442Related DocumentAdapted for Chapter 9
ORCID:
AuthorORCID
Xiao, Liting0000-0003-2703-449X
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:15045
Collection:CaltechTHESIS
Deposited By: Liting Xiao
Deposited On:25 Oct 2022 21:50
Last Modified:25 Oct 2022 21:50

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