7.9k citations · 15.9k across the 76 of their papers we have counts for
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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…
Renormalisation group effects on SMEFT interpretations of LHC data
Rafael Aoude, Fabio Maltoni, Olivier Mattelaer +2
We explore the impact of Renormalisation Group (RG) effects in the Standard Model Effective Field Theory (SMEFT) interpretations of LHC measurements. We implement the RG running an…
Developments in Performance and Portability for MadGraph5_aMC@NLO
Andrea Valassi, Taylor Childers, Laurence Field +6
Event generators simulate particle interactions using Monte Carlo techniques, providing the primary connection between experiment and theory in experimental high energy physics. Th…
Testing the Scalar Triplet Solution to CDF's Fat Problem at the LHC
Jon Butterworth, Julian Heeck, Si Hyun Jeon +2
The Type II Seesaw model remains a popular and viable explanation of neutrino masses and mixing angles. By hypothesizing the existence of a scalar that is a triplet under the weak…
Improving Colour Computations in MadGraph5_aMC@NLO and Exploring a 1/Nc Expansion
Andrew Lifson, Olivier Mattelaer
In this paper, we present an extension of MadGraph5_aMC@NLO which is able to evaluate tree-level QCD matrix-elements up to (one more particle than before). To achieve this…
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…