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Dear all,<br>
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this is a reminder on tomorrow's seminar.<br>
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Best regards,<br>
Carlota, Raza, Miguel<br>
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Date and time: Wednesday, Jan 22, 1:00PM<br>
Room: L102<br>
Speaker: Yang-Ting Chien (YITP, Stony Brook)<br>
Title: Deep Learning Jet Substructure from Two-Particle Correlation<br>
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Abstract: Deciphering the complex information contained in jets produced in collider events requires a physical organization of the jet data. In this talk I will discuss the use of two-particle correlations (2PCs) by pairing individual particles as the initial
jet representation from which a probabilistic model can be built. Particle momenta, as well as particle types and vertex information are included in the correlation. A novel, two-particle correlation neural network (2PCNN) architecture is constructed by combining
neural network based filters on 2PCs and a deep neural network for capturing jet kinematic information. The 2PCNN is applied to boosted boson and heavy flavor tagging, and it achieves excellent performance by comparing to models based on telescoping deconstruction.
Major correlation pairs exploited in the trained models are also identified, which shed light on the physical significance of certain jet substructure.<br>
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Bluejeans connection: https://bluejeans.com/610445877 <br>
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