Publication: Principal components analysis of extensive air showers applied to the identification of cosmic TeV gamma-rays
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2004-11
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University Chicago Press
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We apply a principal components analysis (PCA) to the secondary particle density distributions at ground level produced by cosmic gamma-rays and protons. For this purpose, high-energy interactions of cosmic rays with Earth's atmosphere and the resulting extensive air showers have been simulated by means of the CORSIKA Monte Carlo code. We show that a PCA of the two-dimensional particle density fluctuations provides a decreasing sequence of covariance matrix eigenvalues that have typical features of a polynomial law, which are different for different primary cosmic rays. This property is applied to the separation of electromagnetic showers from proton simulated extensive air showers, and it is proposed as a new discrimination method that can be used experimentally for gamma-proton separation. A cutting parameter related to the polynomial behavior of the decreasing sequence of covariance matrix eigenvalues is calculated, and the efficiency of the cutting procedure for gamma-proton separation is evaluated.
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© 2004. The American Astronomical Society. All rights reserved. This work is supported in part by Spanish Government grants for the research projects BFM2003-04147-C02 and FTN2003-08337-C04-04.
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Aharonian, F. A., et al. 2002, A&A, 390, 39
Atkins, R., et al. 2003, ApJ, 595, 803
Bailer-Jones, C. A. L., Irwin, M., Gilmore, G., & von Hippel, T. 1997, MNRAS, 292, 157
Bock, R. K., et al. 2001, Nucl. Instrum. Methods Phys. Res., 516, 511
Camin, D. V. 2004, Nucl. Instrum. Methods Phys. Res., 518, 172
Chilingaryan, A. A. 1995, Pattern Recognition Lett., 16, 333
Edwards, W., Lindman, H., & Savage, L. J. 1990, in Robustness of Bayesian Analyses, ed. J. B. Kadane ( North Holland: Elsevier)
Faleiro, E., & Contreras, J. L. 1998, J. Phys., G24, 1795
Faleiro, E., & Gómez, J. M. G. 1999, Europhys. Lett., 45, 437
———. 2001, Fluctuation & Noise Lett., 1, L117
Faleiro, E., Gómez, J. M. G., & Relaño, A. 2003, Astropart. Phys., 19, 617
Faleiro, E., Gómez, J. M. G., Relaño, A., & Retamosa, J. 2004, Astropart. Phys., in press
Fegan, D. J. 1997, J. Phys. G, 23, 1013
Gaisser, T. K. 1990, Cosmic Rays and Particle Physics (Cambridge: Cambridge Univ. Press)
Gao, J. B., Cao, Y., & Lee, J.-M. 2003, Phys. Lett. A, 314, 392
Glazebrook, K., Offer, A. R., & Deeley, K. 1998, ApJ, 492, 98
Greisen, K. 1956, Prog. Cosmic Ray Phys., 3, 3
Heck, D., & Knapp, J. 2002, Extensive Air Shower Simulation with CORSIKA ( Karlsruhe: Inst. Kernphys.)
Kamata, K., & Nishimura, J. 1958, Prog. Theor. Phys. Suppl., 6, 93
Karle, A., et al. 1995, Astropart. Phys., 4, 1
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. 1992, Numerical Recipes in FORTRAN (Cambridge: Cambridge: Univ. Press)
Ronen, S., Aragón Salamanca, A., & Lahav, O. 1999, MNRAS, 303, 284
Schafer, B. M., et al. 2001, Nucl. Instrum. Methods Phys. Res., 465, 394
Sokolsky, P. 1989, Introduction to Ultra–high Energy Cosmic Ray Physics (New York: Addison Wesley)
Zacks, S. 1977, The Theory of Statistical Inference (New York: Wiley)