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dc.contributor.authorStone, Connor
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date.accessioned2018-08-28T22:55:50Z
dc.date.available2018-08-28T22:55:50Z
dc.identifier.urihttp://hdl.handle.net/1974/24478
dc.description.abstractOne of the most outstanding mysteries in the Universe is the nature of dark matter. In this thesis, two unique projects that focus on understanding dark matter are presented. First, Machine Learning is used to detect background signals in the DEAP-3600 direct detection dark matter experiment. Machine Learning algorithms are found to achieve a near order of magnitude improvement on discrimination of surface backgrounds ove a standard position reconstruction algorithm. To ward against over-fitting, the algorithms are fit to real data before being tested on simulated data. Validation is done by modifying the detector simulation and recording the change in performance. It is found that the Machine Learning algorithms are robust to small changes in the optics of the detector and so would be stable for use on the real data. The second project is a study of the intrinsic scatter of a scaling relation and its potential tension with the dark matter hypothesis. The Radial Acceleration Relation (RAR) relates the observed radial acceleration in a galaxy to that expected from a purely baryonic mass distribution. Lelli et al. (2017) have suggested that the RAR may have zero intrinsic scatter. If true, this would be inconsistent with the Λ Cold Dark Matter (ΛCDM) paradigm, but a natural prediction of Modified Newtonian Dynamics (MOND). A large sample of galaxies is collated from six independent samples, representing over an order of magnitude increase in the amount of data available to examine this scaling relation. To test the zero scatter hypothesis, a Monte-Carlo simulation with a zero intrinsic scatter assumption is constructed; observational uncertainties are then re-introduced to the data. This technique shows observational uncertainties are too small to account for the observed scatter in the relation. It is found that the RAR is consistent with standard ΛCDM models and that it has an intrinsic scatter of order 0.13 dex (∼ 50 % of observed). Further tests show that these results cannot be explained with non-circular motions or correlations with other galaxy parameters.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectAstrophysicsen_US
dc.subjectAstroparticle Physicsen_US
dc.subjectMachine Learningen_US
dc.subjectGalaxiesen_US
dc.subjectDark Matter Detectionen_US
dc.titleFrom Surface Events in DEAP-3600 to the Radial Acceleration Relation in Spiral Galaxiesen_US
dc.typeThesisen
dc.description.degreeMaster of Scienceen_US
dc.contributor.supervisorNoble, Anthony
dc.contributor.supervisorCourteau, Stéphane
dc.contributor.departmentPhysics, Engineering Physics and Astronomyen_US


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