Monte Carlo event reconstruction implemented with artificial neural networks

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%.