Algorithms for Fast Simulation of a Particle Physics Detector with Neural Oblivious Decision Trees

Student: Krivenkov Danila

Supervisor: Nikita Kazeev

Educational Programme: Software Engineering (Bachelor)

Year of Graduation: 2021

Modern experiments at the Large Hadron Collider (LHC) require a large amount of simulated data. Obtaining such data requires a lot of computing power and takes a lot of processing time. The need for fast simulation only increases every year. Generative adversarial neural networks (GANs) show the best results for solving this problem. In this paper, a method is proposed for simulating the response of a Cherenkov detector from an ensemble of differentiable trees and generative adversarial networks. Tree ensembles perform best on many tabular datasets. The emergence of differentiable trees makes it possible to use them as one of the components in the GAN. We theoretically discuss and experimentally evaluate the pros and cons of the combination of Neural Oblivious Decision Ensembles (NODE) and GAN.

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