Particle discrimination using matched filters and expert neural networks

Author

Soares, W. ; Damazio, D.O. ; Seixas, J.M.

Author_Institution

COPPE, Univ. Fed. do Rio de Janeiro, Brazil

Volume

1

fYear

1999

fDate

1999

Firstpage

595

Abstract

A particle discrimination problem in high-energy physics is addressed by optimal linear filtering and neural processing on experimental data acquired from a highly segmented calorimeter, which is a detector that measures the energy of the incoming particles. It is shown that both approaches are able to identify impurities that typically appear in the data sample and achieve discrimination efficiencies higher than 98%

Keywords

feature extraction; high energy physics instrumentation computing; matched filters; neural nets; particle calorimetry; position sensitive particle detectors; solid scintillation detectors; 98 percent; discrimination efficiencies; expert neural networks; high-energy physics; highly segmented calorimeter; impurity identification; incoming particle energy measurement; matched filters; neural processing; optimal linear filtering; particle discrimination; scintillating tile calorimeter; Detectors; Large Hadron Collider; Matched filters; Maximum likelihood detection; Mesons; Neural networks; Particle measurements; Physics; Prototypes; Tiles;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99. The 6th IEEE International Conference on

Conference_Location

Pafos

Print_ISBN

0-7803-5682-9

Type

conf

DOI

10.1109/ICECS.1999.812355

Filename

812355