Monte Carlo event reconstruction implemented with artificial neural networks

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%.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Physics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Author dc:creator

• Tolley, Emma Elizabeth

Advisor dc:contributor.advisor

• Richard Milner.

• Physics.

Statement dc:rights

• M.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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/65535

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/65535