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Taoli Cheng
(University of Chinese Academy of Sciences)
11/12/2017, 12:50
I am writing to propose a talk based on the recent paper https://arxiv.org/abs/1711.02633.
The main topic is exploring the performance of Recursive Neural Networks in quark/gluon tagging.
Wojciech Fedorko
(UBC), Dr
Wojciech Fedorko
(University of British Columbia)
11/12/2017, 13:10
Multivariate techniques based on engineered features have found wide adoption in the identification of jets resulting from hadronic top decays at the Large Hadron Collider (LHC). Recent Deep Learning developments in this area include the treatment of the calorimeter activation as an image or supplying a list of jet constituent momenta to a fully connected network. This latter approach lends...
Gregor Kasieczka
(Uni Hamburg)
11/12/2017, 13:50
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)...
Isaac Henrion
(NYU)
11/12/2017, 14:10
