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

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)

摘要
本文介绍了一款适配人工神经网络算法的Fortran 77（F77）程序包JETNET 2.0。该程序包的核心服务对象为高能物理领域，但因其通用性较强，同样可应用于任意模式识别相关场景。其核心组件包含多层感知器反向传播算法与拓扑自组织映射网络。本程序包由一系列子程序构成，既可以通过标准配置直接调用，也可便捷地进行修改以适配其他架构……

程序名称：JETNET 2.0
目录编号：ACGV_v1_0

问题属性
高能物理领域存在诸多极具挑战性的模式识别问题：例如基于量能器探测数据区分光子与轻子，或根据强子碎裂产物的运动学特征识别夸克。针对此类模式识别问题的常规解决方案，是在高维数据中引入针对性的截断筛选规则。

Mendeley数据平台CPC程序库中收录的本程序版本：
ACGV_v1_0；JETNET 2.0；DOI:10.1016/0010-4655(92)90099-K
ACGV_v2_0；JETNET 版本3.0；DOI:10.1016/0010-4655(94)90120-1

本程序源自贝尔法斯特女王大学托管的CPC程序库（1969-2019）