113 citations · 137 across the 3 of their papers we have counts for
7 papers · 1 filter
To Profile or To Marginalize -- A SMEFT Case Study
Ilaria Brivio, Sebastian Bruggisser, Nina Elmer +3
Global SMEFT analyses have become a key interpretation framework for LHC physics, quantifying how well a large set of kinematic measurements agrees with the Standard Model. This ag…
Loop Amplitudes from Precision Networks
Simon Badger, Anja Butter, Michel Luchmann +2
Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and…
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…
From Models to SMEFT and Back?
Ilaria Brivio, Sebastian Bruggisser, Emma Geoffray +5
We present a global analysis of the Higgs and electroweak sector, in the SMEFT framework and matched to a UV-completion. As the UV-model we use the triplet extension of the electro…
Understanding Event-Generation Networks via Uncertainties
Marco Bellagente, Manuel Haußmann, Michel Luchmann +1
Following the growing success of generative neural networks in LHC simulations, the crucial question is how to control the networks and assign uncertainties to their event output.…
Per-Object Systematics using Deep-Learned Calibration
Gregor Kasieczka, Michel Luchmann, Florian Otterpohl +1
We show how to treat systematic uncertainties using Bayesian deep networks for regression. First, we analyze how these networks separately trace statistical and systematic uncertai…