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ANN based Multi Classifier System for Prediction of High Energy Shower Primary Energy and Core Location

Authors: Gitanjali Devi, Kandarpa Kumar Sarma, Pranayee Datta, Anjana Kakoti Mahanta


Cosmic showers, during the transit through space, produce sub - products as a result of interactions with the intergalactic or interstellar medium which after entering earth generate secondary particles called Extensive Air Shower (EAS). Detection and analysis of High Energy Particle Showers involve a plethora of theoretical and experimental works with a host of constraints resulting in inaccuracies in measurements. Therefore, there exist a necessity to develop a readily available system based on soft-computational approaches which can be used for EAS analysis. This is due to the fact that soft computational tools such as Artificial Neural Network (ANN)s can be trained as classifiers to adapt and learn the surrounding variations. But single classifiers fail to reach optimality of decision making in many situations for which Multiple Classifier System (MCS) are preferred to enhance the ability of the system to make decisions adjusting to finer variations. This work describes the formation of an MCS using Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN) with data inputs from correlation mapping Self Organizing Map (SOM) blocks and the output optimized by another SOM. The results show that the setup can be adopted for real time practical applications for prediction of primary energy and location of EAS from density values captured using detectors in a circular grid.

Keywords: EAS, Shower, Core, ANN, Location.

Digital Object Identifier (DOI):

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[1] R. A. Mewaldt: "Cosmic Rays", Macmillan Encyclopedia of Physicsavailable at, 1996.
[2] R. Engel: "Theory and Phenomenology of Extensive Air Showers", Forschungszentrum Karlsruhe, Germany- available at, 2005.
[3] D. Hanna, "Application of Neural Nets to Extensive Air-Showers", Proceedings of the 22nd International Cosmic Ray Conference, IUPAP, Volume 4, p: 500, 1991.
[4] J. C. Perrett and J T P M van Stekelenborg, "The applications of neural networks in the core location analysis of extensive air showers", Bartol Research Institute, University of Delaware, 217 Sharp Laboratory, Newark, DE 19716, USA J. Phys. G: Nucl. Pert. Phys. 17, pp:1291-1302, 1991.
[5] H. J. Mayer, "A neural network algorithm for core location analysis at large extended air shower arrays", Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 317, Issues 1-2, pp. 339-345, June, 1992.
[6] P. Sommers: "Extensive Air Showers and Measurement Techniques", Physics - Astrophysics, C. R. Acad. Sci. Paris, t. 4, Serie IV, pp. 1 - 12, 2004.
[7] T. Wibig, "The Artificial Neural Networks as a tool for analysis of the individual Extensive Air Showers data", Preprint submitted to Elsevier Preprint (1 February 2008), available at 9608227v1.
[8] A. Chilingarian, G. Gharagyozyan, S. Ghazaryan, G. Hovsepyan, E. Mamidjanyan, L. Melkumyan, V. Romakhin, A. Vardanyan and S. Sokhoyan: "Study of extensive air showers and primary energy spectra by MAKET-ANI detector on mountain Aragats", Elsavier Journal of Astroparticle Physics, vol. 28, pp. 5871, 2007.
[9] L. Chen and M. S. Kamel: "Design of Multiple Classifier Systems for Time Series Data ", Spinger Lecture Notes in Computer Science, 2005, vol. 3541, pp. 216-225, 2005.
[10] M. T. El-Melegy and S. M. Ahmed: "Neural Networks in Multiple Classifier Systems for Remote-Sensing Image Classification ", Spinger Studies in Fuzziness and Soft Computing, vol. 210, pp. 65-94, 2007.
[11] Z. Suraj, N. E. Gayar, P. Delimata: "A Rough Set Approach to Multiple Classifier Systems", Fundamenta Informaticae, vol. 72, no. 1-3, pp. 393-406, 2006.
[12] F. Roli: "Mini Tutorial on Multiple Classifier Systems", University of Cagliari, Dept. of Electrical and Electronics Eng., Italy, School on the Analysis of Patterns, 2009.
[13] M. Sharma and R. Mammone: "Speech recognition using sub-word neural tree network models and multiple classifier fusion", CAIP Center, Rutgers University, Piscataway, NJ, USA, 1995.
[14] M. Sharma and R. Mammone: "Artificial neural network fusion: Application to Arabic words recognition", Proceedings of ESANN - 2005- European Symposium on Artificial Neural Networks Bruges, (pp. 151 - 156), Belgium, 27-29 April, 2005.
[15] B. Yegnanarayana, Artificial Neural Networks, 1st Ed., PHI, New Delhi, 2003.
[16] S. Haykin, Neural Networks A Comprehensive Foundation, 2nd Ed., Pearson Education, New Delhi, 2003.
[17] M. Hajmeer and I. Basheer: "A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data", Elsavier Journal of Microbiological Methods, vol. 51, pp. 217 226, 2002.
[18] "Probabilistic Neural Network Tutorial", available at looney/cs773b/PNNtutorial.pdf.
[19] C. Kramer, B. Mckay, and J. Belina: "Probabilistic neural network array architecture for ECG classification," Proc. Annu. Int. Conf. IEEE Eng. Medicine Biol., vol. 17, pp. 807808, 1995.
[20] M. T. Musavi, K. H. Chan, D. M. Hummels, and K. Kalantri: "On the generalization ability of neural-network classifier," IEEE Transactions on Pattern Anal. Machine Intell., vol. 16, no. 6, pp. 659663, 1994.
[21] R. D. Romero, D. S. Touretzky, and G. H. Thibadeau: "Optical Chinese character recognition using probabilistic neural networks," Pattern Recognit., vol. 3, no. 8, pp. 12791292, 1997.
[22] D. F. Specht: "Probabilistic neural networks," Neural Networks, vol. 3, no. 1, pp. 109118, 1990.
[23] Y. N. Sun, M. H. Horng, X. Z. Lin, and J. Y. Wang: "Ultrasonic image analysis for liver diagnosis-a-noninvasive alternative to determine liver disease," IEEE Eng. Med. Biol. Mag., vol. 15, no. 1, pp. 93101, 1996.
[24] P. Burrascano: "Learning vector quantization for the probabilistic neural network," IEEE Transactions on Neural Networks, vol. 2, pp. 458461, July 1991.
[25] P. P. Raghu and B. Yegnanarayana: "Supervised texture classification using a probabilistic neural network and constraint satisfaction model," IEEE Transactions on Neural Networks, vol. 9, pp. 516522, May 1998.
[26] "Enhancements to the probabilistic neural networks," Proc. of IEEE Int. Joint Conf. Neural Networks, Baltimore, MD, pp. 761768, 1992.
[27] H. G. C. Traven: "A neural-network approach to statistical pattern classification by semiparametric estimation of a probability density functions," IEEE Transactions on Neural Networks, vol. 2, pp. 366377, 1991.
[28] A. Zaknich: "A vector quantization reduction method for the probabilistic neural network," Proc. IEEE Int. Conf. Neural Networks, Piscataway, NJ, pp. 11171120, 1997.
[29] K. Z. Mao, K. C. Tan, and W. Ser: "Probabilistic Neural-Network Structure Determination for Pattern Classification," IEEE Transactions on Neural Networks, vol. 11, no. 4, July, 2000.
[30] R. Rojas, Neural Networks-A Systematic Introduction, Springer, Berlin, 1996.