TY - JOUR
AU - Rustige, Lennart
AB - Abstract: 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. In a second step, we perform a detailed study of the effect of applying a state-of-the-art particle flow-based reconstruction procedure to the generated showers. We demonstrate that the performance of the model remains high after reconstruction. These results are an important step towards creating a more general simulation tool, where maintaining physics performance after reconstruction is the ultimate target.
TI - New Angles on Fast Calorimeter Shower Simulation
JF - High Energy Physics - Phenomenology
DO - 10.48550/arxiv.2303.18150
DA - 2023-03-31
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/new-angles-on-fast-calorimeter-shower-simulation-J54lemudwg
VL - 2023
IS - 2303
DP - DeepDyve
ER -