Title :
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
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;
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
DOI :
10.1109/ICECS.1999.812355
