Abstract
The mini-ICAL is a magnetized prototype of the proposed iron calorimeter (ICAL) detector of the upcoming India-Based Neutrino Observatory (INO). The mini-ICAL is designed to study the performance of Resistive Plate Chambers (RPCs) in a magnetic field and the efficiency of reconstruction algorithms. In this study, we investigate the possibility of using a deep learning-based algorithm for muon energy reconstruction in the detector. Deep learning models were developed using the data generated from a simplified geometry of mini-ICAL simulated using the Geant4 package. The models are evaluated for their accuracy in predictions and reconstruction time.
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Samuel, D., Omana Kuttan, M., Samalan, A., Murgod, L.P. (2021). Deep Learning-Based Energy Reconstruction of Cosmic Muons in mini-ICAL Detector. In: Behera, P.K., Bhatnagar, V., Shukla, P., Sinha, R. (eds) XXIII DAE High Energy Physics Symposium. DAEBRNS HEPS 2018 2018. Springer Proceedings in Physics, vol 261. Springer, Singapore. https://doi.org/10.1007/978-981-33-4408-2_109
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DOI: https://doi.org/10.1007/978-981-33-4408-2_109
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