172 citations · 821 across the 34 of their papers we have counts for
10 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…
VERaiPHY -- Validation & Evaluation for Robust AI in PHYsics
Gaia Grosso, Ramon Winterhalder, Lydia Brenner +2
Modern machine learning is leading to substantial gains in precision, flexibility, and computational efficiency in fundamental physics. Statistical validation, uncertainty quantifi…
Unifying Generative Models with Path Integrals
Ramon Winterhalder
We formulate generative modeling as a path integral in which flow-based, diffusion-based, variational, and adversarial models arise as different evaluation principles for a single…
The Living Guide of Machine Learning for Particle Physics
Claudius Krause, Ramon Winterhalder, Matthew Feickert +1
We started the Living Review of Machine Learning for Particle Physics (HEP-ML Living Review) in 2020 as a community-maintained, near-comprehensive bibliography of machine learning…
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…
Interpreting Parton Distributions with Shapley Values
Raphaël Bonnet-Guerrini, Stefano Carrazza, Stefano Forte +3
We show that Shapley values can be used to trace how individual parton distributions (PDFs) shape the theory predictions for high-energy observables computed from them. This provid…
The Monte Carlo Ecosystem in High-Energy Physics: A Primer
Melissa van Beekveld, Enrico Bothmann, Andy Buckley +3
Monte Carlo event generators are the central interface between theoretical calculations and experimental measurements in collider physics. Over several decades, a comprehensive and…
Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation
Manuel Haußmann, Ramon Winterhalder, Maria Ubiali
Reliable uncertainty quantification is essential for the use of machine learning in physics, where scientific discoveries depend on validated probabilistic statements. We provide a…
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…