PoS - Proceedings of Science
Volume 429 - The 6th International Workshop on Deep Learning in Computational Physics (DLCP2022) - Track1. Machine Learning in Particle Astrophysics and High Energy Physics
A machine learning approach to identify the air shower cores for the GRAPES-3 experiment
M. Chakraborty*, S. Ahmad, A. Chandra, S.R. Dugad, U.D. Goswami, S.K. Gupta, B. Hariharan, Y. Hayashi, P. Jagadeesan, A. Jain, P. Jain, S. Kawakami, H. Kojima, S. Mahapatra, P.K. Mohanty, R. Moharana, Y. Muraki, P.K. Nayak, T. Nonaka, A. Oshima, S. Paul, B.P. Pant, D. Pattanaik, G.S. Pradhan, M. Rameez, K. Ramesh, L.V. Reddy, R. Sahoo, R. Scaria, S. Shibata, K. Tanaka, F. Varsi and M. Zuberiet al. (click to show)
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Pre-published on: November 14, 2022
Published on: December 06, 2022
The GRAPES-3 experiment located in Ooty consists of a dense array of 400 plastic scintillator detectors spread over an area of 25,000 $m^2$ and a large area (560 $m^2$) tracking muon telescope. Everyday, the array records about 3 million showers in the energy range of 1 TeV - 10 PeV induced by the interaction of primary cosmic rays in the atmosphere. These showers are reconstructed in order to find several shower parameters such as shower core, size, and age. High-energy showers landing far away from the array often trigger the array and are found to have their reconstructed cores within the array even though their true cores lie outside, due to reconstruction of partial information. These showers contaminate and lead to an inaccurate measurement of energy spectrum and composition. Such showers are removed by applying quality cuts on various shower parameters, manually as well as with machine learning approach. This work describes the improvements achieved in removal of such contaminated showers with the help of machine learning.
DOI: https://doi.org/10.22323/1.429.0001
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