163 citations · 663 across the 18 of their papers we have counts for
20 papers
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…
Simulation-Prior Independent Neural Unfolding Procedure
Anja Butter, Theo Heimel, Nathan Huetsch +2
Machine learning allows unfolding high-dimensional spaces without binning at the LHC. The new SPINUP method extracts the unfolded distribution based on a neural network encoding th…
Profile Likelihoods on ML-Steroids
Theo Heimel, Tilman Plehn, Nikita Schmal
Profile likelihoods, for instance, describing global SMEFT analyses at the LHC are numerically expensive to construct and evaluate. Especially profiled likelihoods are notoriously…
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…
The Landscape of Unfolding with Machine Learning
Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov +9
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new…
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…
Returning CP-Observables to The Frames They Belong
Jona Ackerschott, Rahool Kumar Barman, Dorival Gonçalves +2
Optimal kinematic observables are often defined in specific frames and then approximated at the reconstruction level. We show how multi-dimensional unfolding methods allow us to re…
How to Understand Limitations of Generative Networks
Ranit Das, Luigi Favaro, Theo Heimel +3
Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustr…
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…
Two Invertible Networks for the Matrix Element Method
Anja Butter, Theo Heimel, Till Martini +2
The matrix element method is widely considered the ultimate LHC inference tool for small event numbers. We show how a combination of two conditional generative neural networks enco…
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…
Generative Networks for Precision Enthusiasts
Anja Butter, Theo Heimel, Sander Hummerich +4
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…
Measuring QCD Splittings with Invertible Networks
Sebastian Bieringer, Anja Butter, Theo Heimel +4
QCD splittings are among the most fundamental theory concepts at the LHC. We show how they can be studied systematically with the help of invertible neural networks. These networks…
QCD or What?
Theo Heimel, Gregor Kasieczka, Tilman Plehn +1
Autoencoder networks, trained only on QCD jets, can be used to search for anomalies in jet-substructure. We show how, based either on images or on 4-vectors, they identify jets fro…