276 citations · 882 across the 11 of their papers we have counts for
4 papers · 1 filter
Top quark physics in the Large Hadron Collider era
Michael Russell
We explore various aspects of top quark phenomenology at the Large Hadron Collider and proposed future machines. After summarising the role of the top quark in the Standard Model (…
Deep-learned Top Tagging with a Lorentz Layer
Anja Butter, Gregor Kasieczka, Tilman Plehn +1
We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its…
Top quark electroweak couplings at future lepton colliders
Christoph Englert, Michael Russell
We perform a comparative study of the reach of future collider options for the scale of non-resonant new physics effects in the top quark sector, phrased in the language o…
Deep-learning Top Taggers or The End of QCD?
Gregor Kasieczka, Tilman Plehn, Michael Russell +1
Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepT…