In this video from PASC18, Kamil Deja presents: Using Generative Models for Fast Cluster Simulation in TPC Detector for the ALICE Experiment.
“Simulation of the events happening in the particle detector is a key component of many High Energy Physics experiments. Currently used Monte Carlo techniques allow to do it accurately, but their precision often comes at the expense of relatively high computational cost. In this work, we present a proof-of-concept solution for simulating clusters that occur after particle collision in the TPC detector in the ALICE Experiment at CERN. The new method we propose, dubbed ParticleGAN for simplicity, leverages recently developed Generative Adversarial Networks to learn the trajectories of particle tracks after collision. Although the quality of generated events is not even with the currently used solutions yet, ParticleGAN offer up to 10^3 speedups over the existing approaches. This applies also to other evaluated generative models namely Variational Autoencoders and variants of GANs. In this work we outline current bottlenecks of the proposed approach and discuss further steps that can allow to deploy the proposed generative models for simulation in production.”
Co-Author(s): Tomasz Trzcinski, Łukasz Graczykowski (Warsaw University of Technology, Poland)
Thanks to Rich Brueckner from insideHPC Media Publications for recording the video.