Muon physics and neural network event classifier for the Sudbury Neutrino Observatory

Details Muon physics and neural network event classifier for the Sudbury Neutrino Observatory Muon physics and neural network event classifier for the Sudbury Neutrino Observatory

Dec 1998

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1998phdt.......227c&link_type=abstract

Thesis (PhD). UNIVERSITY OF GUELPH (CANADA), Source DAI-B 59/06, p. 2820, Dec 1998, 125 pages.

Mathematics

Probability

1

Scientific paper

The Sudbury Neutrino Observatory (SNO) has been designed principally to study solar neutrinos and other sources of neutrinos such as supernova neutrinos and atmospheric neutrinos. The SNO heavy water Cerenkov detector will be able to observe all three flavors of neutrinos and allow us to determine the probability of neutrino flavor oscillation. It is hoped that SNO will provide answers to the questions posed by the solar neutrino problem and the atmospheric neutrino anomaly. In order for the experiment to be successful, it is important to fully understand muon interactions. First, muons may produce an important source of background for solar neutrino detection. Secondly, the detection of high-energy atmospheric neutrinos depends on detection of muons produced by the neutrino interaction either inside the detector or in the material surrounding the detector. The processes induced by stopping muons and muon-nucleus interaction are of great importance in a water Cerenkov detector as they produce secondary particles. Muon capture and muon decay processes have been studied in detail. The routines describing theses processes have been implemented in the SNOMAN code to study the detector response. A model to describe muon-nucleus deep inelastic scattering is proposed. In particular, the attempts to parameterize the secondary hadron multiplicity due to deep inelastic scattering are made. In addition, the hadron transport code has been added to SNOMAN for the simulation of the secondary hadron transport and subsequent Cerenkov photon production. Full Monte Carlo simulation of muon transport down to the SNO detector depth has been performed to understand the kinematic properties of cosmic-ray muons entering the SNO detector. Based on the results of the simulations, a simplified method to generate muon flux deep underground has been developed. The usage of pattern recognition techniques with Artificial Neural Networks has been investigated for the event-type classification. It has been observed that a sequential processing of PMT hit patterns by a combination of feed-forward networks can distinguish different classes of solar neutrino events on an event- by-event basis.

Affiliated with

Also associated with

No associations

Rating

Muon physics and neural network event classifier for the Sudbury Neutrino Observatory does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Muon physics and neural network event classifier for the Sudbury Neutrino Observatory, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Muon physics and neural network event classifier for the Sudbury Neutrino Observatory will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-764418