SciPost Phys. 10, 144 (2021) ·
published 14 June 2021
We verify Standard Model Effective Field Theory Ward identities to one loop order when background field gauge is used to quantize the theory. The results we present lay the foundation of next to leading order automatic generation of results in the SMEFT, in both the perturbative and non-perturbative expansion using the geoSMEFT formalism, and background field gauge.
Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn
SciPost Phys. 10, 139 (2021) ·
published 10 June 2021
A critical question concerning generative networks applied to event
generation in particle physics is if the generated events add statistical
precision beyond the training sample. We show for a simple example with
increasing dimensionality how generative networks indeed amplify the training
statistics. We quantify their impact through an amplification factor or
equivalent numbers of sampled events.
Sebastian Bieringer, Anja Butter, Theo Heimel, Stefan Höche, Ullrich Köthe, Tilman Plehn, Stefan T. Radev
SciPost Phys. 10, 126 (2021) ·
published 2 June 2021
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 work with sub-jet information to extract fundamental
parameters from jet samples. Our approach expands the LEP measurements of QCD
Casimirs to a systematic test of QCD properties based on low-level jet
observables. Starting with an toy example we study the effect of the full
shower, hadronization, and detector effects in detail.