Optimizing Neural Network Classifiers with ROOT on a Rocks Linux Cluster

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

Abstract

We present a study to optimize multi-layer perceptron (MLP) classification power with a Rocks Linux cluster [1]. Simulated data from a future high energy physics experiment at the Large Hadron Collider (LHC) is used to teach a neural network to separate the Higgs particle signal from a dominant background [2].
The MLP classifiers have been implemented using the ROOT data analysis framework [3]. Our aim is to reach a stable physics signal recognition for new physics and a well understood background rejection. We report on the physics performance of new neural classifiers developed in this study. We have used the benchmarking capabilities of ROOT and of the Parallel ROOT facility (PROOF) [4] to compare the performance of the Linux clusters at our campus.
Original languageEnglish
Title of host publicationApplied Parallel Computing. State of the Art in Scientific Computing : 8th International Workshop, PARA 2006, Umeå, Sweden, June 18-21, 2006, Revised Selected Papers
Number of pages9
Volume4699/2007
PublisherSpringer
Publication date2007
Pages1065-1073
ISBN (Print)3-540-75754-6
DOIs
Publication statusPublished - 2007
MoE publication typeB2 Book chapter

Publication series

NameLecture Notes in Computer Science
Publisherspringer
Volume4699
ISSN (Print)0302-9743

Fields of Science

  • 113 Computer and information sciences

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