163 citations · 663 across the 18 of their papers we have counts for
5 papers · 1 filter
MadNIS, Ready for Production at LO
Theo Heimel, Olivier Mattelaer, Daniele Massaro +1
We extend neural multi-channel importance sampling in MadNIS to full proton-proton processes in a production-ready event-generation setup. We benchmark a wide range of LHC processe…
An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning
Maximilian Dax, Theo Heimel, Gilles Louppe
Simulation-based inference (SBI) with machine learning is an increasingly important tool for solving inverse problems in science and engineering, including parameter inference and…
Neural Control Variates at LO and NLO
Theo Heimel, Tilman Plehn, Rebecca Revelli +2
We employ neural control variates to minimize the range of event weights and avoid negative weights for phase-space integration and event generation. A signed control variate, buil…
MadNIS at NLO
Giovanni De Crescenzo, Javier Mariño Villadamigo, Nina Elmer +4
We combine fast amplitude surrogates with neural importance sampling to accelerate NLO calculations. For virtual corrections, a learned ratio to the Born matrix element with calibr…
MadSpace -- Event Generation for the Era of GPUs and ML
Theo Heimel, Olivier Mattelaer, Ramon Winterhalder
MadSpace is a new modular phase-space and event-generation library written in C++ with native GPU support via CUDA and HIP. It provides a unified compute-graph-based framework for…