Oct 19 – 23, 2020
Europe/Zurich timezone

High Fidelity Simulation of High Granularity Calorimeters with High Speed

Oct 23, 2020, 3:05 PM
5m
Lightning talk 3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model Workshop

Speaker

Engin Eren (Deutsches Elektronen-Synchrotron DESY)

Description

In this talk, we investigate the use of Generative Adversarial Networks (GANs) and a new architecture -- the Bounded Information Bottleneck Autoencoder (Bib-AE) -- for modeling electromagnetic showers in the central region of the Silicon-Tungsten calorimeter of the proposed International Large Detector. An accurate simulation of differential distributions including for the first time the shape of the minimum-ionizing-particle peak compared to a full GEANT4 simulation for a high-granularity calorimeter with 27k simulated channels have been achieved. Our results further strengthen the case of using generative networks for fast simulation and demonstrate that physically relevant differential distributions can be described with high accuracy. Furthermore, a detailed investigation of the latent space encoded by Bib-AE has been carried out.

Primary authors

Engin Eren (Deutsches Elektronen-Synchrotron DESY) Sascha Daniel Diefenbacher (Hamburg University (DE))

Presentation materials