Speaker
Gregor Kasieczka
(Uni Hamburg)
Description
Distinguishing hadronic top quark decays from light quark and gluon jets (top tagging) is an important tool for new physics searches at the LHC and allows the comparison of different machine learning approaches. We present results on using convolutional neural networks as well as recent studies employing a physics motivated network architecture based on Lorentz Invariance (and not much else) for top tagging. We also discuss further generalisations of this approach.
Primary author
Gregor Kasieczka
(Uni Hamburg)
