Abstract
High Energy Physics (HEP) experiments require discrimination of a few interesting events among a huge number of background events generated during an experiment. Hierarchical triggering hardware architectures are needed to perform this tasks in real-time. In this paper three neural network models are studied as possible candidate for such systems. A modified Multi-Layer Perceptron (MLP) architecture and a EαNet architecture are compared against a traditional MLP. Test error below 25% is archived by all architectures in two different simulation strategies. EαNet performance are 1 to 2%better on test error with respect to the other two architectures using the smaller network topology. The design of a digital implementation of the proposed neural network is also outlined.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Beri, S.B., Bhat, P., Kaur, R., and Prosper, H. (2000). Top quark mass measurements using neural networks. Proc. of VII International Workshop on Advanced Computing and Analysis Techniques in Physics Research.
Bhat, C. and Bhat, P. (2000). Using ensembles of neural networks in hep analysis. Proc. of VII International Workshop on Advanced Computing and Analysis Techniques in Physics Research.
Ciobanu, C., Hughes, R., and Winer, B. (1999). Using neural networks to identify single top. CDF Note CDF/ANAL/TOP/GROUP/5370.
Fent, J., Froechtenicht, W.,.Gaede, F, H. Getta, D. Goldner, and A. Gruber (1996). The realization of a second level neural network trigger for the h1 experiment at hera. Proc. of Fifth International Workshop On Software Engineering, Artificial Intelligence, Neural Nets, Genetic Algorithms, Symbolic Algebra, Automatic Calculation.
Gaglio, S., Pilato, G., Sorbello, F., and Vassallo, G. (2000). Using the hermite regression formula to design a neural architecture with automatic learning of the hidden activation functions. Lecture Notes in Artificial Intelligence, Springer-Verlag, 1792:226–237.
Hays, C. and Kotwal, A.V. (2002). Using a neural network for electron identification. CDF Note CDF/DOC/ELECTRON/CDFR/5810.
Janauschek, L., Dichtl, J., Eberl, M., Enzenberger, M., and Fent, J. (1999). Artificial neural networks as a second level trigger at the h1 experiment at hera performance analysis and physics results. Proc. of Sixth International Workshop On Software Engineering, Artificial Intelligence, Neural Nets, Genetic Algorithms, Symbolic Algebra, Automatic Calculation.
Pilato, G., Sorbello, F., and Vassallo, G. (2001). An innovative way to measure the quality of a neural network without the use of the test set. International Journal of Artificial Computational Intelligence, 5:31–36.
Powell, M.J.D. (1968). Restart procedures for the conjugate gradient method. Mathematical Programming, 12:241–254.
Sorbello, F., Gioiello, G.A.M., and Vitabile, S. (1999). Handwritten character recognition using a mlp. L.C. Jain and B. Lazzerini Eds., Knowledge-Based Intelligent Techniques in Character Recognition, CRC Press, pages 91–119.
Tuttle, J.P., Hays, C., and Kotwal, A.V. (2001). Neural networks for electron and photon identification. CDF Note CDF/DOC/ELECTRON/CDFR/5791.
Vitabile, S., Gentile, A., and Sorbello, F. (2002). A neural network based automatic road signs recognizer. Proc. of IEEE World Congress on Computational Intelligence — International Joint Conference on Neural Networks, 3:2315–2320.
Vitabile, S., Gentile, A., and Sorbello, F. (2004). Real-time road signs recognition on a simd architecture. WSEAS Transactions on Circuits and Systems, 3:664–669.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer
About this paper
Cite this paper
Vitabile, S., Pilato, G., Vassallo, G., Siniscalchi, S.M., Gentile, A., Sorbello, F. (2005). Neural Classification of HEP Experimental Data. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Biological and Artificial Intelligence Environments. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3432-6_18
Download citation
DOI: https://doi.org/10.1007/1-4020-3432-6_18
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3431-2
Online ISBN: 978-1-4020-3432-9
eBook Packages: Computer ScienceComputer Science (R0)