240 citations · 611 across the 7 of their papers we have counts for
8 papers
The Machine Learning Landscape of Top Taggers
G. Kasieczka, T. Plehn, A. Butter +24
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established metho…
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…
Reports of My Demise Are Greatly Exaggerated: -subjettiness Taggers Take On Jet Images
Liam Moore, Karl Nordström, Sreedevi Varma +1
We compare the performance of a convolutional neural network (CNN) trained on jet images with dense neural networks (DNNs) trained on n-subjettiness variables to study the distingu…
Interpreting top-quark LHC measurements in the standard-model effective field theory
J. A. Aguilar Saavedra, C. Degrande, G. Durieux +32
This note proposes common standards and prescriptions for the effective-field-theory interpretation of top-quark measurements at the LHC.
Results from TopFitter
Andy Buckley, Christoph Englert, James Ferrando +5
We discuss a global fit of top quark BSM couplings, phrased in the model-independent language of higher-dimensional effective operators, to the currently available data from the LH…
Giving top quark effective operators a boost
Christoph Englert, Karl Nordstrom, Liam Moore +1
We investigate the prospects to systematically improve generic effective field theory-based searches for new physics in the top sector during LHC run 2 as well as the high luminosi…