Creating of GAN for Fast Generation of Calorimeters Responses in Elementary Particle Detectors in CERN

Student: Belotskii Valerii

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Applied Mathematics (Bachelor)

Year of Graduation: 2021

Nowadays experiments in high energy physics require a lot of simulated data, while the currently used Monte- Carlo simulation algorithm of calorimeter responses, despite their precision, are the most computationally expensive and time- consuming part of the simulation. Recent studies show that generative models can speed up the process of such simulation by several orders of magnitude, but with some loss of precision. The present study aims to find ways to increase models precision by looking for appropriate GAN models architectures, ways to condition inputs, loss functions.

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