The MadNIS Reloaded

Abstract

In pursuit of precise and fast theory predictions for the LHC, we present an implementation of the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS further enhance its efficiency and speed. We validate this implementation for realistic partonic processes and find significant gains from using modern machine learning in event generators.

15 pages, 6 figures, 2 tables; v3: updates incl. referee requests

References in corpus (12)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• An Introduction to PYTHIA 8.2
• NICE: Non-linear Independent Components Estimation
• Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
• Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
• CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
• Design and engineering of a simplified workflow execution for the MG5aMC event generator on GPUs and vector CPUs
• One-loop matrix element emulation with factorisation awareness
• End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
• Developments in Performance and Portability for MadGraph5_aMC@NLO
• EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
• CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation

Cited by in corpus (21)

• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• A Lorentz-Equivariant Transformer for All of the LHC
• Normalizing Flows for High-Dimensional Detector Simulations
• A Portable Parton-Level Event Generator for the High-Luminosity LHC
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Differentiable MadNIS-Lite
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• Sampling NNLO QCD phase space with normalizing flows
• PIPPIN: Generating variable length full events from partons
• Accelerating multijet-merged event generation with neural network matrix element surrogates
• Unifying Simulation and Inference with Normalizing Flows
• Amplitude Uncertainties Everywhere All at Once
• Extrapolating Jet Radiation with Autoregressive Transformers
• Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
• Amplitude Surrogates for Multi-Jet Processes
• How to Unfold Top Decays
• Observable Optimization for Precision Theory: Machine Learning Energy Correlators
• Monte Carlo Event Generators
• Variational inference for pile-up removal at hadron colliders with diffusion models
• Monte Carlo Event Generators for Future Lepton Colliders
• NNLO QCD corrections to $\gamma \gamma \to Q\bar{Q}$ from Local Unitarity combined with Coulomb resummation and NLO EW effects