Tutorial on neural network applications in high-energy physics: A 1992 perspective

Conference · Wed Apr 01 00:00:00 EST 1992

OSTI ID:5132840

Denby, Bruce H. ^[1]

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1. Fermilab

Feed forward and recurrent neural networks are introduced and related to standard data analysis tools. Tips are given on applications of neural nets to various areas of high energy physics. A review of applications within high energy physics and a summary of neural net hardware status are given.

• Research Organization: Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 5132840
• Report Number(s): FERMILAB-CONF-92-121-E; CDF-PUB-CDF-PUBLIC-1737; FNAL-C-92-121-E; oai:inspirehep.net:335595
• Country of Publication: United States
• Language: English

Related Subjects

HIGH ENERGY PHYSICS
NEURAL NETWORKS
USES
COMPUTER ARCHITECTURE
DATA ANALYSIS
PARTICLE IDENTIFICATION
QUARK MATTER
TRAINING
TRIGGER CIRCUITS
EDUCATION
ELECTRONIC CIRCUITS
MATTER
PHYSICS
PULSE CIRCUITS
665000* - Physics of Condensed Matter- (1992-)
990200 - Mathematics & Computers