20-22 March 2017
Europe/Zurich timezone
There is a live webcast for this event.

Deep-learning Top Taggers or The End of QCD?

22 Mar 2017, 09:45
222-R-001 (CERN)



Note: MAIN AUDITORIUM for the opening session Monday morning
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Gregor Kasieczka (Eidgenoessische Technische Hochschule Zuerich (CH))



Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model production channel. Using standard fat jets we then compare its performance to a multivariate QCD-based top tagger. We find that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach for multivariate hypothesis-based top tagging.

Presentation Materials

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