CaloShowerGAN, a Generative Adversarial Networks model for fast calorimeter shower simulation

Abstract

In particle physics, the demand for rapid and precise simulations is rising. The shift from traditional methods to machine learning-based approaches has led to significant advancements in simulating complex detector responses. CaloShowerGAN is a new approach for fast calorimeter simulation based on Generative Adversarial Network (GAN). We use Dataset 1 of the Fast Calorimeter Simulation Challenge 2022 to demonstrate the efficacy of the model to simulate calorimeter showers produced by photons and pions. The dataset is originated from the ATLAS experiment, and we anticipate that this approach can be seamlessly integrated into the ATLAS system. This development brings a significant improvement compared to the deployed GANs by ATLAS and could offer great enhancement to the current ATLAS fast simulations.

26 pages, 17 figures, 5 tables, Inspire https://inspirehep.net/literature/2697185

References in corpus (14)

• Scikit-learn: Machine Learning in Python
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• Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters

Cited by in corpus (8)

• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Normalizing Flows for High-Dimensional Detector Simulations
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles
• Choose Your Diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation
• ParaFlow: fast calorimeter simulations parameterized in upstream material configurations