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

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

Student: Kamlyk Eryk

Supervisor: Fedor Ratnikov

Faculty: Faculty of Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2021

Fast generation of calorimeter responses in elementary particle detectors is one of the current challenges of high energy physics. Increasing capacity of particle accelerators like Large Hadron Collider creates a need for algorithms that are less computationally complex. In this paper we present a deep learning framework based on Generative Adversarial Networks that increases the computation speed while preserving good quality of simulation. We present a new way of decomposing the data into constant and stochastic components and learning them separately. We also present an analysis of convergence of distributions of physical properties and study the applicability of GANs to the problem of their accurate reproduction.

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