Pattern recognition in high energy physics with artificial neural networks — JETNET 2.0

Published: 1 January 1992| Version 1 | DOI: 10.17632/k65chwcbmf.1
Contributors:
Leif Lönnblad,
Carsten Peterson,
Thorsteinn Rögnvalsson

Description

Abstract A F77 package of adaptive artificial neural network algorithms, JETNET 2.0, is presented. Its primary target is the high energy physics community, but it is general enough to be used in any pattern-recognition application area. The basic ingredients are the multilayer perceptron back-propagation algorithm and the topological self-organizing map. The package consists of a set of subroutines, which can either be used with standard options or be easily modified to host alternative architectures ... Title of program: JETNET 2.0 Catalogue Id: ACGV_v1_0 Nature of problem High energy physics offers many challenging pattern recognition problems. It could be separating photons from leptons based on calorimeter information or the identification of a quark based on the kinematics of the hadronic fragmentation products. Standard procedures for such recognition problems is the introduction of relevant cuts in the multi-dimensional data. Versions of this program held in the CPC repository in Mendeley Data ACGV_v1_0; JETNET 2.0; 10.1016/0010-4655(92)90099-K ACGV_v2_0; JETNET VERSION 3.0; 10.1016/0010-4655(94)90120-1 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

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