Speaker
Description
For high-energy physics experiments, the generation of Monte Carlo events, and particularly the simulation of the detector response, is a very computationally intensive process. In many cases, the primary bottleneck in detector simulation is the detailed simulation of the electromagnetic and hadronic showers in the calorimeter system.
ATLAS is currently using its state-of-the-art fast simulation tool AtlFast3, which employs a combination of histogram-based parameterizations and Generative Adversarial Networks (GANs) to provide a highly efficient yet accurate simulation of the full detector response.
Motivated by the Fast Calorimeter Simulation Challenge, which concluded with a community paper demonstrating the superiority of modern generative models — such as diffusion models, transformers and (continuous) normalizing flows — over more traditional approaches like GANs and variational autoencoders, the applicability of these next-generation techniques to the ATLAS fast calorimeter simulation was explored.
In this talk, first physics performance results of these novel models are presented. The models are trained on a newly generated input dataset with extended pseudorapidity coverage and optimized granularity that allows to reproduce the detailed simulation with a reduced number of voxels.
Significance
This presentation will provide first performance results of new generative models for fast calorimeter simulation that are expected to replace the currently used GAN-based simulation in ATLAS's state-of-the-art fast simulation toolkit AtlFast3
| Experiment context, if any | ATLAS Experiment |
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