16-21 July 2017
Embassy Suites Buffalo
US/Eastern timezone

Deep-learning Top Taggers and No End to QCD

19 Jul 2017, 14:30
20m
Embassy Suites Buffalo

Embassy Suites Buffalo

200 Delaware Avenue Buffalo, NY 14202

Speaker

Gregor Kasieczka (Eidgenoessische Technische Hochschule Zuerich (CH))

Description

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 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 show that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach for multivariate hypothesis-based top tagging. Finally, we will comment on new results using machine-learning-based subjet methods.

Primary author

Gregor Kasieczka (Eidgenoessische Technische Hochschule Zuerich (CH))

Presentation Materials