Abstract
Electromagnetic calorimeter (ECal) is an important detector of the Multi-Purpose Detector (MPD) at the Nuclotron-based Ion Collider fAcility (NICA) at the Joint Institute for Nuclear Research (JINR), Dubna. In that experiment, the main goal of the MPD ECal (which was selected to be a shashlyk-type electromagnetic calorimeter) is to identify electrons, photons and measure their energies with high precision. This paper introduces the tests of the IHEP-produced ECal module prototypes using muons from the accelerator U-70 (IHEP, Protvino, Russia). Those tests demonstrated the capability to collect 700 photoelectrons per MIP particle. To optimize its performance, a deep-learning-based algorithm is used to perform the energy reconstruction of the ECal module prototypes made by Tsinghua University (China) using the data from a beam test in DESY. For the 4.0 GeV electron beam, an energy resolution better than 3.8% can be achieved. This improves the energy characteristics of ECal compared to the more traditional reconstruction methods applied in the calorimetr.
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