Neural network trigger algorithms for heavy quark event selection in a fixed target high energy physics experiment

Abstract

The study of particles containing heavy quarks is currently a major topic in high energy physics. In this paper, neural net trigger algorithms are developed to distinghish heavy quark (signal) events from light quark (background) events in a fixed target experiment. The event tracks which are parametrized by the impact parameter D and the angle Φ of the track with respect to the beam line, vary in number and in position in the Φ- D plane. An invariant second-order moment feature set and an invariant D-sequence representation are derived to characterize the signal and background event track patterns in the Φ- D plane. A three-layer perceptron is trained to classify events as signal/background via the moments and D-sequences. A nearest neighbor classifier is also developed to serve as a benchmark for comparing the performance of the neural net triggers. Results indicate that the selected moment feature set and the D-sequence representation contain essential signal/background discriminatory information. The results also show that the neural network trigger algorithms are superior to the nearest neighbor trigger algorithms. A very high discrimination against background events and a very high efficiency for selecting signal events is obtained with the D-sequence neural net trigger algorithm.