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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.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/14571
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.language.isoenen
dc.publisherMontana State University - Bozeman, College of Letters & Scienceen
dc.subject.lcshGravitational waves.en
dc.subject.lcshBlack holes (Astronomy).en
dc.subject.lcshNeutron stars.en
dc.subject.lcshAlgorithms.en
dc.titleDetecting and characterizing gravitational waves with minimal assumptionsen
dc.typeDissertationen
dc.rights.holderCopyright 2018 by Margaret Ann Millhouseen
thesis.degree.committeemembersMembers, Graduate Committee: Neil J. Cornish (chairperson); 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
mus.data.thumbpage48en


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