Neural network tagging in a toy model

被引：1

作者：

Milek, M ^{[1
]}

Patel, P ^{[1
]}

机构：

[1] McGill Univ, Dept Phys, Montreal, PQ H3A 2T8, Canada

来源：

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT | 1999年 / 425卷 / 03期

关键词：

artificial neural networks; high-energy physics; tagging; Monte Carlo;

D O I：

10.1016/S0168-9002(98)01445-4

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

The purpose of this study is a comparison of Artificial Neural Network approach to HEP analysis against the traditional methods. A toy model used in this analysis consists of two types of particles defined by four generic properties. A number of "events" was created according to the model using standard Monte Carlo techniques. Several fully connected, feed forward multi layered Artificial Neural Networks were trained to tag the model events. The performance of each network was compared to the standard analysis mechanisms and significant improvement was observed. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：577 / 588

页数：12

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