A Method for Estimation of the Parameters of the Primary Particle of an Extensive Air Shower by a High-Altitude Detector
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A method for estimation of the parameters of the primary particle of an extensive air shower (EAS) by a high-altitude detector complex is described. This method was developed as part of the Pamir-XXI project. The results may be useful for other high-altitude projects and the EAS method in general. The specific configurations of optical detectors for Cherenkov EAS radiation and charged-particle detectors, the methods for data processing, and the attainable accuracy of reconstruction of parameters of primary particles (energy, direction, mass/type) are presented. The results primarily cover optical detectors that are suitable for studying EASs from primary nuclei in the range of energies E0 = 100 TeV–100 PeV and showers from primary γ-quanta with energies of Eγ ≥ 30 TeV. Grids of charged-particle detectors designed to determine the EAS direction and energy in the E0 = 1 PeV–1 EeV range are also considered. The obtained accuracy estimates are the upper limits of the actual experimental accuracies.
Keywordsextensive air showers Cherenkov light statistical modeling statistical pattern recognition
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- 1.A. S. Borisov and V. I. Galkin, J. Phys.: Conf. Ser. 409, 012089 (2013). doi 10.1088/1742-6596/409/1/012089Google Scholar
- 2.R. A. Antonov, S. P. Beschapov, E. A. Bonvech, et al., J. Phys.: Conf. Ser. 409, 012088 (2013). doi 10.1088/1742-6596/409/1/012088Google Scholar
- 4.D. Heck and T. Pierog, CORSIKA User’s Guide (Karlsruhe Inst. for Technology, 2011).Google Scholar
- 5.K. Greisen, in Progress in Cosmic Ray Physics, Ed. by J. G. Wilson (North-Holland, Amsterdam, 1956), Vol. 3, p.1.Google Scholar
- 11.J. Linsley, in Proc. 15th Int. Cosmic Ray Conf., Plovdiv, Bulgaria, 1977, Vol. 12, p.89.Google Scholar
- 12.J. Linsley, in Proc. 16th Int. Cosmic Ray Conf., Kyoto, Japan, 1979, Vol. 9, p.274.Google Scholar
- 20.K. Fukunaga, Introduction to Statistical Pattern Recognition (Academic, New York, 1972).Google Scholar
- 21.GEANT4 Collab., GEANT4 User’s Guide for Application Developers. Version 10.1 (2014).Google Scholar