Generative Networks for Precision Enthusiasts

Abstract

Generative networks are opening new avenues in fast event generation for the LHC. We show how generative flow networks can reach percent-level precision for kinematic distributions, how they can be trained jointly with a discriminator, and how this discriminator improves the generation. Our joint training relies on a novel coupling of the two networks which does not require a Nash equilibrium. We then estimate the generation uncertainties through a Bayesian network setup and through conditional data augmentation, while the discriminator ensures that there are no systematic inconsistencies compared to the training data.

28 pages, 14 figures

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• How to Understand Limitations of Generative Networks
• Two Invertible Networks for the Matrix Element Method
• The MadNIS Reloaded
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• What's Anomalous in LHC Jets?
• Returning CP-Observables to The Frames They Belong
• CaloFlow for CaloChallenge Dataset 1
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• The Landscape of Unfolding with Machine Learning
• Precision-Machine Learning for the Matrix Element Method
• ELSA -- Enhanced latent spaces for improved collider simulations
• Normalizing Flows for High-Dimensional Detector Simulations
• Calorimeter shower superresolution
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Differentiable MadNIS-Lite
• Towards a data-driven model of hadronization using normalizing flows
• Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
• End-to-end simulation of particle physics events with Flow Matching and generator Oversampling
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• Hypergraphs in LHC Phenomenology -- The Next Frontier of IRC-Safe Feature Extraction
• Advancing Tools for Simulation-Based Inference
• Anomaly detection with flow-based fast calorimeter simulators
• PIPPIN: Generating variable length full events from partons
• Jet Classification Using High-Level Features from Anatomy of Top Jets
• Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
• Generative Invertible Quantum Neural Networks
• Reconstructing axion-like particles from beam dumps with simulation-based inference
• Unifying Simulation and Inference with Normalizing Flows
• Generative Unfolding with Distribution Mapping
• Top-philic Machine Learning
• Extrapolating Jet Radiation with Autoregressive Transformers
• How to Unfold Top Decays
• Encoding off-shell effects in top pair production in Direct Diffusion networks