This thesis compares the performance of Probability Density Estimation and Neural Networks as applied to the identification of tau leptons and electrons at the DO detector for Run II. The theory behind each method of multivariate analysis is briefly described. The efficiencies of each of the methods are compared from analysis of Monte Carlo data samples, and optimal choices for the discrimination between signal and background are made.
| Identifer | oai:union.ndltd.org:RICE/oai:scholarship.rice.edu:1911/17401 |
| Date | January 2001 |
| Creators | Askew, Andrew Warren |
| Contributors | Padley, B. Paul |
| Source Sets | Rice University |
| Language | English |
| Detected Language | English |
| Type | Thesis, Text |
| Format | 99 p., application/pdf |
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