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The Search for Gravitational Waves from the Coalescence of Black Hole Binary Systems in Data from the LIGO and Virgo Detectors. Or: A Dark Walk through a Random Forest

Citation

Hodge, Kari Alison (2014) The Search for Gravitational Waves from the Coalescence of Black Hole Binary Systems in Data from the LIGO and Virgo Detectors. Or: A Dark Walk through a Random Forest. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/HY26-2059. https://resolver.caltech.edu/CaltechTHESIS:06022014-104457554

Abstract

The LIGO and Virgo gravitational-wave observatories are complex and extremely sensitive strain detectors that can be used to search for a wide variety of gravitational waves from astrophysical and cosmological sources. In this thesis, I motivate the search for the gravitational wave signals from coalescing black hole binary systems with total mass between 25 and 100 solar masses. The mechanisms for formation of such systems are not well-understood, and we do not have many observational constraints on the parameters that guide the formation scenarios. Detection of gravitational waves from such systems — or, in the absence of detection, the tightening of upper limits on the rate of such coalescences — will provide valuable information that can inform the astrophysics of the formation of these systems. I review the search for these systems and place upper limits on the rate of black hole binary coalescences with total mass between 25 and 100 solar masses. I then show how the sensitivity of this search can be improved by up to 40% by the the application of the multivariate statistical classifier known as a random forest of bagged decision trees to more effectively discriminate between signal and non-Gaussian instrumental noise. I also discuss the use of this classifier in the search for the ringdown signal from the merger of two black holes with total mass between 50 and 450 solar masses and present upper limits. I also apply multivariate statistical classifiers to the problem of quantifying the non-Gaussianity of LIGO data. Despite these improvements, no gravitational-wave signals have been detected in LIGO data so far. However, the use of multivariate statistical classification can significantly improve the sensitivity of the Advanced LIGO detectors to such signals.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:machine learning, black holes, random forest of bagged decision trees, LIGO, Virgo, gravitational waves, data quality, multivariate statistical classification, separation of signal and noise, artificial neural network, support vector machine, detector characterization, compact binary coalescence
Degree Grantor:California Institute of Technology
Division:Physics, Mathematics and Astronomy
Major Option:Physics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Weinstein, Alan Jay
Group:LIGO
Thesis Committee:
  • Weinstein, Alan Jay (chair)
  • Golwala, Sunil
  • Libbrecht, Kenneth George
  • Chen, Yanbei
Defense Date:12 May 2014
Non-Caltech Author Email:hodge.kari (AT) gmail.com
Record Number:CaltechTHESIS:06022014-104457554
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:06022014-104457554
DOI:10.7907/HY26-2059
ORCID:
AuthorORCID
Hodge, Kari Alison0000-0002-1025-0420
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:8463
Collection:CaltechTHESIS
Deposited By: Kari Hodge
Deposited On:03 Jun 2014 18:57
Last Modified:01 Sep 2020 22:37

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