Deep learning algorithms have gained importance in particle physics in the last few years. They have been shown to outperform traditional strategies in particle identification, tracking and energy reconstruction in the most modern high-energy physics experiments. The attractive feature of these techniques is their ability to model large dimensionality inputs and catch nontrivial correlations among the variables, which could be hidden or not easy to model. This paper focuses on the application of deep neural networks to the event reconstruction of the Limadou High-Energy Particle Detector on board the China Seismo-Electromagnetic Satellite. The core of the reconstruction chain is a set of fully connected neural networks that reconstructs the nature, the arrival direction and the kinetic energy of incoming electrons and protons, starting from the signals recorded in the detector. These networks are trained on a dedicated Monte Carlo simulation as representative as possible of real data. We describe the simulation, architecture and methodology adopted to design and train the networks, and finally report on the performance measured on simulated and flight data.

Deep learning based event reconstruction for the Limadou High-Energy Particle Detector

Cristoforetti, M.;Gebbia, G.;
2022

Abstract

Deep learning algorithms have gained importance in particle physics in the last few years. They have been shown to outperform traditional strategies in particle identification, tracking and energy reconstruction in the most modern high-energy physics experiments. The attractive feature of these techniques is their ability to model large dimensionality inputs and catch nontrivial correlations among the variables, which could be hidden or not easy to model. This paper focuses on the application of deep neural networks to the event reconstruction of the Limadou High-Energy Particle Detector on board the China Seismo-Electromagnetic Satellite. The core of the reconstruction chain is a set of fully connected neural networks that reconstructs the nature, the arrival direction and the kinetic energy of incoming electrons and protons, starting from the signals recorded in the detector. These networks are trained on a dedicated Monte Carlo simulation as representative as possible of real data. We describe the simulation, architecture and methodology adopted to design and train the networks, and finally report on the performance measured on simulated and flight data.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/330874
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