activity
20152020
most citedMachine Learning Uncertainties with Adversarial Neural Networks

73 citations · 194 across the 10 of their papers we have counts for

collaborators

10 papers

hep-ph2020★ 10 cited

Electroweak Top Couplings, Partial Compositeness and Top Partner Searches

Stephen Brown, Christoph Englert, Peter Galler +1

Partial top quark compositeness is a crucial aspect of theories with strong electroweak symmetry breaking. Together with the heavy top partners that lift the top quark mass to its…

hep-ph2019★ 17 cited

Effective field theory and scalar extensions of the top quark sector

Christoph Englert, Peter Galler, Chris D. White

Effective field theory (EFT) approaches are widely used at the LHC, such that it is important to study their validity, and ease of matching to specific new physics models. In this…

hep-ph2019★ 6 cited

Proposal for the validation of Monte Carlo implementations of the standard model effective field theory

Gauthier Durieux, Ilaria Brivio, Fabio Maltoni +38

We propose a procedure to cross-validate Monte Carlo implementations of the standard model effective field theory. It is based on the numerical comparison of squared amplitudes com…

hep-ph2019

TopFitter: Fitting top-quark Wilson Coefficients to Run II data

Stephen Brown, Andy Buckley, Christoph Englert +7

We describe the latest TopFitter analysis, which uses top quark observables to fit the Wilson Coefficients of the SM augmented with dimension-6 operators. In particular, we discuss…

hep-ph2019★ 29 cited

Approaching robust EFT limits for CP-violation in the Higgs sector

Christoph Englert, Peter Galler, Andrew Pilkington +1

Constraining CP-violating interactions in effective field theory (EFT) of dimension six faces two challenges. Firstly, degeneracies in the multi-dimensional space of Wilson coeffic…

hep-ph2018★ 73 cited

Machine Learning Uncertainties with Adversarial Neural Networks

Christoph Englert, Peter Galler, Philip Harris +1

Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial tas…