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dc.contributor.authorBaker, Paul T.
dc.contributor.authorCaudill, Sarah
dc.contributor.authorHodge, Kari A.
dc.contributor.authorTalukder, Dipongkar
dc.contributor.authorCapano, Collin
dc.contributor.authorCornish, Neil J.
dc.date.accessioned2015-07-13T18:14:45Z
dc.date.available2015-07-13T18:14:45Z
dc.date.issued2015-03
dc.identifier.citationBaker, Paul T., Sarah Caudill, Kari A. Hodge, Dipongkar Talukder, Collin Capano, and Neil J. Cornish. “Multivariate Classification with Random Forests for Gravitational Wave Searches of Black Hole Binary Coalescence.� Phys. Rev. D 91, no. 6 (March 2015). doi:10.1103/physrevd.91.062004.en_US
dc.identifier.issn1550-7998
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/9181
dc.description.abstractSearches for gravitational waves produced by coalescing black hole binaries with total masses ≳25M⊙ use matched filtering with templates of short duration. Non-Gaussian noise bursts in gravitational wave detector data can mimic short signals and limit the sensitivity of these searches. Previous searches have relied on empirically designed statistics incorporating signal-to-noise ratio and signal-based vetoes to separate gravitational wave candidates from noise candidates. We report on sensitivity improvements achieved using a multivariate candidate ranking statistic derived from a supervised machine learning algorithm. We apply the random forest of bagged decision trees technique to two separate searches in the high mass (≳25M⊙) parameter space. For a search which is sensitive to gravitational waves from the inspiral, merger, and ringdown (IMR) of binary black holes with total mass between 25M⊙ and 100M⊙, we find sensitive volume improvements as high as 70±13−109±11\% when compared to the previously used ranking statistic. For a ringdown-only search which is sensitive to gravitational waves from the resultant perturbed intermediate mass black hole with mass roughly between 10M⊙ and 600M⊙, we find sensitive volume improvements as high as 61±4−241±12\% when compared to the previously used ranking statistic. We also report how sensitivity improvements can differ depending on mass regime, mass ratio, and available data quality information. Finally, we describe the techniques used to tune and train the random forest classifier that can be generalized to its use in other searches for gravitational waves.en_US
dc.subjectPhysicsen_US
dc.subjectBlack holesen_US
dc.subjectGravityen_US
dc.titleMultivariate Classification with Random Forests for Gravitational Wave Searches of Black Hole Binary Coalescenceen_US
dc.typeArticleen_US
mus.citation.issue6en_US
mus.citation.journaltitlePhysical Review Den_US
mus.citation.volume91en_US
mus.identifier.categoryPhysics & Mathematicsen_US
mus.identifier.doi10.1103/physrevd.91.062004en_US
mus.relation.collegeCollege of Letters & Scienceen_US
mus.relation.departmentPhysics.en_US
mus.relation.universityMontana State University - Bozemanen_US


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