A30 Computerized and centralized participant entry in large-scale, multi-centered clinical trials

Abstract

Since a neural network (NN) approach has been shown to be applicable to the problem of Higgs boson detection at LHC [I. Iashvili, A. Kharchilava, CMS TN-1996/100; M. Mjahed, Nucl. Phys. B 140 (2005) 799], we study the use of NNs in the problem of tagging b jets in pp→bb¯HSUSY, HSUSY→ττ in the Compact Muons Solenoid experiment [F. Hakl, et al., Nucl. Instr. and Meth. A 502 (2003) 489; S. Lehti, CMS NOTE-2001/019; G. Segneri, F. Palla, CMS NOTE-2002/046]. B tagging is an important tool for separating the Higgs events with associated b jets from the Drell–Yan background Z,γ*→ττ, for which the associated jets are mostly light quark and gluon jets.We teach multi-layer perceptrons (MLPs) available in the object oriented implementation of data analysis framework ROOT [ROOT—An Object Oriented Data Analysis Framework, in: Proceedings of the AIHENP’96 Workshop, Lausanne, September 1996, Nucl. Instr. and Meth. A 389 (1997) 81]. The following learning methods are evaluated: steepest descent algorithm, (BFGS) Broyden–Fletcher–Goldfarb–Shanno algorithm, and variants of conjugate gradients. The ROOT code generation feature of standalone C++ classifiers is utilized.We compare the b tagging performance of MLPs with another ROOT based feed forward NN tool NeuNet [J.P. Ernenwein, NeuNet software for ROOT], which uses a common back-propagation learning method. In addition, we demonstrate the use of the self-organizing map program package (SOM_PAK) and the learning vector quantization program package (LVQ_PAK) [T. Kohonen, et al., SOM_PAK: the self-organizing map program package, Technical Report A31; T. Kohonen, et al., LVQ_PAK: the learning vector quantization program package, Technical Report A30, Laboratory of Computer and Information Science, Helsinki University of Technology, FIN-02150 Espoo, Finland, 1996] in the b tagging problem.