Speaker
Description
Simulating detector response for the Monte Carlo-generated
collisions is a key component of every high-energy physics experiment.
The methods used currently for this purpose provide high-fidelity re-
sults, but their precision comes at a price of high computational cost.
In this work, we present a proof-of-concept solution for simulating the
responses of detector clusters to particle collisions, using the real-life
example of the TPC detector in the ALICE experiment at CERN. An
essential component of the proposed solution is a generative model that
allows to simulate synthetic data points that bear high similarity to
the real data. Leveraging recent advancements in machine learning, we
propose to use state-of-the-art generative models, namely Variational
Autoencoders (VAE) and Generative Adversarial Networks (GAN), that
prove their usefulness and efficiency in the context of computer vision
and image processing.
The main advantage offered by those methods is a significant speed up
in the execution time, reaching up to the factor of 103 with respect to
the Geant 3. Nevertheless, this computational speedup comes at a price
of a lower simulation quality and in this work we show quantitative
and qualitative proofs of those limitations of generative models. We also
propose further steps that will allow to improve the quality of the models
and lead to their deployment in production environment of the TPC
detector.
| Intended contribution length | 30 minutes |
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