Optimizing Neural Network Classifiers with ROOT on a Rocks Linux Cluster
نویسندگان
چکیده
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]. We utililize features of the Parallel ROOT facility (PROOF) [4] to analyze our data and to understand the functionality of the neural networks. PROOF is designed for interactive parallel data analysis of large data sets. Our aim is to reach a stable physics signal recognition for new physics and a well understood background rejection. We report on the performance of PROOF and on the integration of PROOF with the cluster environment in use and on the physics performance of new neural classifiers developed in this study.
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