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

Exploring the DNN performance in Jet Physics

Not scheduled
20m
Embassy Suites Buffalo

Embassy Suites Buffalo

200 Delaware Avenue Buffalo, NY 14202

Speaker

Taoli Cheng (University of Chinese Academy of Sciences)

Description

Since the machine learning techniques are improving rapidly, it has been shown that the image recognition technique can be used to detect jet substructure. And it turns out that deep neural networks can match or outperform traditional approach. To push it further, we investigate the Recursive Neural Networks (RecNN), which embeds jet clustering history recursively as in natural language processing, with particle flow information implemented. In this way, we can have the data input in a most complete and effective way. We show its performance in jet observables and indicate its potential in help detect Higgs signals at the LHC.

Primary author

Taoli Cheng (University of Chinese Academy of Sciences)

Presentation Materials

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