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Monte Carlo event reconstruction implemented with artificial neural networks

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Physics, 2011. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 41). / I implemented event reconstruction of a Monte Carlo simulation using neural networks. The OLYMPUS Collaboration is using a Monte Carlo simulation of the OLYMPUS particle detector to evaluate systematics and reconstruct events. This simulation registers the passage of particles as 'hits' in the detector elements, which can be used to determine event parameters such as momentum and direction. However, these hits are often obscured by noise. Using Geant4 and ROOT, I wrote a program that uses artificial neural networks to separate track hits from noise and reconstruct event parameters. The classification network successfully discriminates between track hits and noise for 97.48% of events. The reconstruction networks determine the various event parameters to within 2-3%. / by Emma Elizabeth Tolley. / S.B.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/65535
Date January 2011
CreatorsTolley, Emma Elizabeth
ContributorsRichard Milner., Massachusetts Institute of Technology. Dept. of Physics., Massachusetts Institute of Technology. Dept. of Physics.
PublisherMassachusetts Institute of Technology
Source SetsM.I.T. Theses and Dissertation
LanguageEnglish
Detected LanguageEnglish
TypeThesis
Format41 p., application/pdf
RightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission., http://dspace.mit.edu/handle/1721.1/7582

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