GANplifying Event Samples

Abstract

A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample. We show for a simple example with increasing dimensionality how generative networks indeed amplify the training statistics. We quantify their impact through an amplification factor or equivalent numbers of sampled events.

15 pages, 7 figures, fixed two equations, extended acknowledgments, addressed referee comments, improved figure readability

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• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
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• Comparing Machine Learning and Interpolation Methods for Loop-Level Calculations
• Precision-Machine Learning for the Matrix Element Method
• Unsupervised Quantum Circuit Learning in High Energy Physics
• Multi-variable Integration with a Neural Network
• Normalizing Flows for High-Dimensional Detector Simulations
• Non-Parametric Data-Driven Background Modelling using Conditional Probabilities
• Morphological Classification of Radio Galaxies with wGAN-supported Augmentation
• Differentiable MadNIS-Lite
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Exploring phase space with Nested Sampling
• Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
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• Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• Calibrating Bayesian Generative Machine Learning for Bayesiamplification
• Quark-versus-gluon tagging in CMS Open Data with CWoLa and TopicFlow
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• Amplitude Uncertainties Everywhere All at Once
• Extrapolating Jet Radiation with Autoregressive Transformers
• CaloHadronic: a diffusion model for the generation of hadronic showers
• How to Unfold Top Decays
• CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Generators and the (Accelerated) Future