Detecting and characterizing gravitational waves with minimal assumptions

dc.contributor.advisorChairperson, Graduate Committee: Neil J. Cornishen
dc.contributor.authorMillhouse, Margaret Annen
dc.date.accessioned2018-10-02T21:04:54Z
dc.date.available2018-10-02T21:04:54Z
dc.date.issued2018en
dc.description.abstractAfter many years of preparation and anticipation, we are finally in the era of routine gravitational-wave detection. All of the detected signals so far have come from merging compact objects-- either black holes or neutron stars. These are signals for which we have very good waveform models, but there still exist other more poorly modeled sources as well as the possibility of completely new gravitational-wave sources. Because of this, it is important to have the ability to confidently detect gravitational-waves from a wide variety of sources. In this Thesis I will describe one particular algorithm used to detect and characterize gravitational-wave signals using Bayesian inference techniques, and minimal assumptions on the source of the gravitational wave. I will report on the methods and results of the implementation of this search in the first two observing runs of advanced LIGO. I will also discuss developments to this algorithm to improve waveform reconstruction, and target certain signals without using full waveform templates.en
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/14571en
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Letters & Scienceen
dc.rights.holderCopyright 2018 by Margaret Ann Millhouseen
dc.subject.lcshGravitational wavesen
dc.subject.lcshBlack holes (Astronomy)en
dc.subject.lcshNeutron starsen
dc.subject.lcshAlgorithmsen
dc.titleDetecting and characterizing gravitational waves with minimal assumptionsen
dc.typeDissertationen
mus.data.thumbpage48en
thesis.degree.committeemembersMembers, Graduate Committee: Nico Yunes; Dana Longcope; Greg Francis; Charles C. Kankelborg.en
thesis.degree.departmentPhysics.en
thesis.degree.genreDissertationen
thesis.degree.namePhDen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage199en

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
MillhouseM0518.pdf
Size:
3.63 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
826 B
Format:
Plain Text
Description:
Copyright (c) 2002-2022, LYRASIS. All rights reserved.