172 citations · 821 across the 34 of their papers we have counts for
31 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…
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…
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…
Amplitude Surrogates for Multi-Jet Processes
Luca Beccatini, Fabio Maltoni, Olivier Mattelaer +1
Accurate and efficient amplitude predictions are essential for precision studies of multi-jet processes at the LHC. We introduce a novel neural network architecture that predicts m…
FASTColor -- Full-color Amplitude Surrogate Toolkit for QCD
Javier Mariño Villadamigo, Rikkert Frederix, Tilman Plehn +2
High-multiplicity events remain a bottleneck for LHC simulations due to their computational cost. We present a ML-surrogate approach to accelerate matrix element reweighting from l…
Amplitude Uncertainties Everywhere All at Once
Henning Bahl, Nina Elmer, Tilman Plehn +1
Ultra-fast, precise, and controlled amplitude surrogates are essential for future LHC event generation. First, we investigate the noise reduction and biases of network ensembles an…
The Physics Behind ML-based Quark-Gluon Taggers
Sophia Vent, Ramon Winterhalder, Tilman Plehn
Jet taggers provide an ideal testbed for applying explainability techniques to powerful ML tools. For theoretically and experimentally challenging quark-gluon tagging, we first ide…
BitHEP -- The Limits of Low-Precision ML in HEP
Claudius Krause, Daohan Wang, Ramon Winterhalder
The increasing complexity of modern neural network architectures demands fast and memory-efficient implementations to mitigate computational bottlenecks. In this work, we evaluate…
Accurate Surrogate Amplitudes with Calibrated Uncertainties
Henning Bahl, Nina Elmer, Luigi Favaro +3
Neural networks for LHC physics have to be accurate, reliable, and controlled. Using neural surrogates for the prediction of loop amplitudes as a use case, we first show how activa…
Differentiable MadNIS-Lite
Theo Heimel, Olivier Mattelaer, Tilman Plehn +1
Differentiable programming opens exciting new avenues in particle physics, also affecting future event generators. These new techniques boost the performance of current and planned…
Full and approximated NLO predictions for like-sign W-boson scattering at the LHC
Stefan Dittmaier, Christopher Schwan, Ramon Winterhalder
We report on a recent calculation of next-to-leading-order (NLO) QCD and electroweak corrections to like-sign W-boson scattering at the Large Hadron Collider, including all partoni…
The MadNIS Reloaded
Theo Heimel, Nathan Huetsch, Fabio Maltoni +3
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…
Precision-Machine Learning for the Matrix Element Method
Theo Heimel, Nathan Huetsch, Ramon Winterhalder +2
The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integratio…
Like-Sign W-Boson Scattering at the LHC -- Approximations and Full Next-to-Leading-Order Predictions
Stefan Dittmaier, Philipp Maierhöfer, Christopher Schwan +1
We present a new calculation of next-to-leading-order corrections of the strong and electroweak interactions to like-sign W-boson scattering at the Large Hadron Collider, implement…
ELSA -- Enhanced latent spaces for improved collider simulations
Benjamin Nachman, Ramon Winterhalder
Simulations play a key role for inference in collider physics. We explore various approaches for enhancing the precision of simulations using machine learning, including interventi…
MadNIS -- Neural Multi-Channel Importance Sampling
Theo Heimel, Ramon Winterhalder, Anja Butter +5
Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normal…
Modern Machine Learning for LHC Physicists
Tilman Plehn, Anja Butter, Barry Dillon +3
Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do…
Machine Learning and LHC Event Generation
Anja Butter, Tilman Plehn, Steffen Schumann +48
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predicti…
Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
Anja Butter, Sascha Diefenbacher, Gregor Kasieczka +4
The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set…