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Gravity Spy and X-Pypeline: A multidisciplinary approach to characterizing and understanding non-astrophysical gravitational wave data and its impact on searches for unmodelled signals

Coughlin, Scott 2019. Gravity Spy and X-Pypeline: A multidisciplinary approach to characterizing and understanding non-astrophysical gravitational wave data and its impact on searches for unmodelled signals. PhD Thesis, Cardiff University.
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Abstract

With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) has initiated a new field of astronomy by providing an alternate means of sensing the Universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of aLIGO from non-gravitational-wave disturbances. Nonetheless, aLIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate that the possibility of accidental coincidence between the two aLIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are allowed by the design sensitivity of aLIGO. For this reason, over the past few years, glitch classification techniques have been developed to help make this task easier. Specifically, I explore the effect of glitches, and their suppression, on key gravitational-wave searches such as that for a Galactic supernova. Moreover, I explore the impact of including machine learning techniques in the post-processing stage of the gravitational-wave search algorithm, “X-Pypeline”. When performing a two detector network search for a gravitational wave from a Galactic supernova, this thesis finds that including information about glitch families and using machine learning techniques in the post-processing stages of the analysis can improve the sensitive range of the search by 10-15 percent over the standard post-processing method.

Item Type: Thesis (PhD)
Date Type: Completion
Status: Unpublished
Schools: Physics and Astronomy
Subjects: Q Science > QB Astronomy
Q Science > QC Physics
Uncontrolled Keywords: gravitational waves, machine learning, supernovae
Funders: Cardiff University
Date of First Compliant Deposit: 17 October 2019
Last Modified: 17 Oct 2019 13:15
URI: http://orca-mwe.cf.ac.uk/id/eprint/126086

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