New Angles on Fast Calorimeter Shower Simulation

Understand

The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools.

• Machine learning based generative models offer a solution that is both fast and accurate.
• In this work we extend the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture, designed for the simulation of particle showers in highly granular calorimeters, in two key directions.
• First, we generalise the model to a multi-parameter conditioning scenario, while retaining a high degree of physics fidelity.