Neural networks with statistical preprocessing for particle discrimination in high-energy physics

Enrico Pasqualucci

doi:10.1117/12.205114

6 April 1995 Neural networks with statistical preprocessing for particle discrimination in high-energy physics

Enrico Pasqualucci

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205114
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

A typical task of an analysis program in high energy physics is the discrimination between different kinds of particles interacting with a detector. Neural networks can be easily used to perform this task. In this paper, the performances of a feed-forward neural network as a particle identifier are studied and compared with results from discriminant analysis. A typical task, the (pi) -(mu) separation at 250 MeV/c is presented as an example application. Experimental data collected during a test run of the KLOE electromagnetic calorimeter are used. The effects of the introduction of a statistical pre-processing on physical variables is studied. It allows us to obtain better results both in terms of learning time and in terms of efficiency and background rejection.

Citation Download Citation

Enrico Pasqualucci "Neural networks with statistical preprocessing for particle discrimination in high-energy physics", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205114

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available