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

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

OSTI ID:10154341

Denby, B

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., Batavia, IL (United States)
• Sponsoring Organization: USDOE, Washington, DC (United States)
• DOE Contract Number: AC02-76CH03000
• OSTI ID: 10154341
• Report Number(s): FNAL/C-92/121-E; CONF-920172-7; ON: DE92016003; IN: CDF/PUB/CDF/PUBLIC/1737
• Resource Relation: Conference: 2. international workshop on software engineering, artificial intelligence (AI), and neural nets for high energy and nuclear physics,L`Agelonde (France),13-18 Jan 1992; Other Information: PBD: Apr 1992
• Country of Publication: United States
• Language: English

Related Subjects

75 CONDENSED MATTER PHYSICS
SUPERCONDUCTIVITY AND SUPERFLUIDITY
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
NEURAL NETWORKS
USES
HIGH ENERGY PHYSICS
DATA ANALYSIS
QUARK MATTER
TRAINING
COMPUTER ARCHITECTURE
TRIGGER CIRCUITS
PARTICLE IDENTIFICATION
665000
990200
PHYSICS OF CONDENSED MATTER
MATHEMATICS AND COMPUTERS