Creating of GAN for the Fast Generation of Calorimeters Responses in Elementary Particle Detectors in Elementary Particle Detector\'s in CERN

Student: Fakanov Pavel

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2021

Simulation of calorimeter responses plays a vital role in nuclear and particle physics. The existing approach to the generation of calorimeters responses is not applicable to the generation of all kinds due to its performance limitations. As part of this work, we proposed an approach to fast calorimeter response generation with time components in elementary particle detectors in CERN using generative adversarial networks. We applied Wasserstein Generative Adversarial Networks with gradient penalty and experimented with other architectures for signals generation. Our approach allows to achieve over 10,000 speed-up over the Geant4 simulation software.

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