Speaker
Michael Russell
(Heidelberg University)
Description
We show how a novel network architecture based on Lorentz Invariance (and not much else) can be used to identify hadronically decaying top quarks. We compare its performance to alternative approaches, including convolutional neural networks, and find it to be very competitive.
We also demonstrate how this architecture can be extended to include tracking information and show its application to a multi-class identification problem in Higgs physics.
