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VERSION:2.0
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BEGIN:VEVENT
DTSTAMP:20261018T123018Z
LAST-MODIFIED:20180919T173745Z
DTSTART:20180924T190000Z
DTEND:20180924T200000Z
UID:event2018@bu.edu
URL:http://physics.bu.edu/internal/events/show/2018
SUMMARY:Solar Axion Search in SNO+ with  Machine Learning
DESCRIPTION:Featuring Aobo Li\n\nPart of the Preliminary Oral Exam.\n\nExam
	ining Committee:\n\nChris Grant(PI\, High Energy Experiment) Indara Suarez 
	(High Energy Experiment)\, Claudio Rebbi(High Energy Theory)\, Mark Kon(BU 
	Math Dept.)\n\nAbstract:  \n\nLiquid scintillator-based (LS) detectors are 
	one of the leading detector technologies in the search for neutrinoless dou
	ble-beta decay. It has been demonstrated that such a detector can also be a
	dopted to search for Axion Like Particle from solar. They are currently lim
	ited by naturally occurring and spallation induced backgrounds. Here we use
	 a convolutional neural network\, a common algorithm from computer vision\,
	 to attempt to distinguish between events that would have made it through e
	xisting cuts. We train our network on Monte Carlo simulated truth data with
	 a range of detector capabilities\, and evaluate the training results in th
	ese different conditions. The ultimate goal of this project is to apply sop
	histicated machine learning techniques to reject backgrounds in real detect
	or data.
LOCATION:SCI 352\, 590 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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