Monte Carlo event reconstruction implemented with artificial neural networks
Name
746920295-MIT.pdf
Description
Full printable version
Size
1.72 MB
Format
Adobe PDF
Checksum (MD5)
1a445e5264646a0ae15657df2988ff24
Author(s)
Tolley, Emma Elizabeth
Advisor(s)
Richard Milner.
Date Issued
2011
Publisher
Massachusetts Institute of Technology
Abstract
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%.
Description
Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Physics, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 41).
Subjects
Physics.
MIT Department
Massachusetts Institute of Technology. Department of Physics
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