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Home > DeepJet: a deep-learned multiclass jet-tagger for slim and fat jets
 
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Title DeepJet: a deep-learned multiclass jet-tagger for slim and fat jets
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Author(s) Gouskos, Loukas (speaker) (Univ. of California Santa Barbara (US)) ; Qu, Huilin (speaker) (Univ. of California Santa Barbara (US)) ; Stoye, Markus (speaker) (CERN) ; Kieseler, Jan (speaker) (CERN) ; Verzetti, Mauro (speaker) (CERN)
Corporate author(s) CERN. Geneva
Imprint 2018-04-09. - 0:26:30.
Series (Machine Learning)
(2nd IML Machine Learning Workshop)
Lecture note on 2018-04-09T11:00:00
Subject category Machine Learning
Abstract We present a customized neural network architecture for both, slim and fat jet tagging. It is based on the idea to keep the concept of physics objects, like particle flow particles, as a core element of the network architecture. The deep learning algorithm works for most of the common jet classes, i.e. b, c, usd and gluon jets for slim jets and W, Z, H, QCD and top classes for fat jets. The developed architecture promising gains in performance as shown in simulation of the CMS collaboration. Currently the tagger is under test in real data in the CMS experiment.
Copyright/License © 2018-2024 CERN
Submitted by paul.seyfert@cern.ch

 


 Record created 2018-04-13, last modified 2022-11-02
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