# Deep-learning Top Taggers or The End of QCD?

- Institution: CERN
- Department: Machine Learning
- Speaker: Gregor Kasieczka
- Year: 2017
- Language: English
- Subjects: Computer science
- Source: [CERN](https://videos.cern.ch/record/3017247)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

https://arxiv.org/abs/1701.08784

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..

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## Lectures

1. [Deep-learning Top Taggers or The End of QCD?](https://invisiblenetwork.ai/learn/cern/3017247/11567e53de8a), 23 min (CERN's player)