Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters

Abstract

High-precision modeling of subatomic particle interactions is critical for many fields within the physical sciences, such as nuclear physics and high energy particle physics. Most simulation pipelines in the sciences are computationally intensive -- in a variety of scientific fields, Generative Adversarial Networks have been suggested as a solution to speed up the forward component of simulation, with promising results. An important component of any simulation system for the sciences is the ability to condition on any number of physically meaningful latent characteristics that can effect the forward generation procedure. We introduce an auxiliary task to the training of a Generative Adversarial Network on particle showers in a multi-layer electromagnetic calorimeter, which allows our model to learn an attribute-aware conditioning mechanism.

7 pages, 5 figures, in proceedings of the 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017)

References in corpus (7)

• NIPS 2016 Tutorial: Generative Adversarial Networks
• Wasserstein GAN
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• Neural Networks for Full Phase-space Reweighting and Parameter Tuning
• Unfolding with Generative Adversarial Networks
• A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties
• Fast Point Cloud Generation with Diffusion Models in High Energy Physics
• CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
• PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
• Calomplification -- The Power of Generative Calorimeter Models
• L2LFlows: Generating High-Fidelity 3D Calorimeter Images
• DCTRGAN: Improving the Precision of Generative Models with Reweighting
• How to Understand Limitations of Generative Networks
• CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models
• Electromagnetic Showers Beyond Shower Shapes
• Towards a Deep Learning Model for Hadronization
• Inductive Simulation of Calorimeter Showers with Normalizing Flows
• CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows
• The MadNIS Reloaded
• PC-Droid: Faster diffusion and improved quality for particle cloud generation
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• CaloFlow for CaloChallenge Dataset 1
• Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Precision-Machine Learning for the Matrix Element Method
• Normalizing Flows for High-Dimensional Detector Simulations
• Parameter Estimation using Neural Networks in the Presence of Detector Effects
• Calorimeter shower superresolution
• Parametrizing the Detector Response with Neural Networks
• Differentiable MadNIS-Lite
• Learning Multivariate New Physics
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• CaloShowerGAN, a Generative Adversarial Networks model for fast calorimeter shower simulation
• RadioGAN - Translations between different radio surveys with generative adversarial networks
• Uncertainties associated with GAN-generated datasets in high energy physics
• Anomaly detection with flow-based fast calorimeter simulators
• Variational Autoencoders for Jet Simulation
• Latent Space Refinement for Deep Generative Models
• BitHEP -- The Limits of Low-Precision ML in HEP
• CaloHadronic: a diffusion model for the generation of hadronic showers
• CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation