81 citations · 175 across the 16 of their papers we have counts for
3 papers · 1 filter
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…
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.…
Deep-Learning Jets with Uncertainties and More
Sven Bollweg, Manuel Haussmann, Gregor Kasieczka +3
Bayesian neural networks allow us to keep track of uncertainties, for example in top tagging, by learning a tagger output together with an error band. We illustrate the main featur…