5 research outputs found
Statistical Pattern Recognition: Application to Oscillation Searches Based on Kinematic Criteria
Classic statistical techniques (like the multi-dimensional likelihood and the
Fisher discriminant method) together with Multi-layer Perceptron and Learning
Vector Quantization Neural Networks have been systematically used in order to
find the best sensitivity when searching for
oscillations. We discovered that for a general direct appearance
search based on kinematic criteria: a) An optimal discrimination power is
obtained using only three variables (, and
) and their correlations. Increasing the number of variables (or
combinations of variables) only increases the complexity of the problem, but
does not result in a sensible change of the expected sensitivity. b) The
multi-layer perceptron approach offers the best performance. As an example to
assert numerically those points, we have considered the problem of
appearance at the CNGS beam using a Liquid Argon TPC detector.Comment: 24 pages, 15 figure
Neutrino physics at accelerators
Present and future neutrino experiments at accelerators are mainly concerned
with understanding the neutrino oscillation phenomenon and its implications.
Here a brief account of neutrino oscillations is given together with a
description of the supporting data. Some current and planned accelerator
neutrino experiments are also explained.Comment: 23 pages, 24 figures. Talk given at the Corfu Summer Institute on
Elementary Particle Physics 200