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11–14 Aug 2023
学术报告厅
Asia/Shanghai timezone

Symmetry Preserving Attention Networks for resolved top quark reconstruction at the LHC

Not scheduled
20m
学术报告厅

学术报告厅

青岛蓝谷国际酒店
Talk Machine Learning

Speaker

Prof. Hideki Okawa (高能所)

Description

The reconstruction of top-quarks from their decay components is a complex problem which limits the sensitivity of many analyses. A novel approach to this problem, utilizing Symmetry Preserving Attention Networks, has been previously presented for all-hadronic ttbar decays. In this talk, we present new features implemented in the algorithm as well as its extended application to semi-leptonic ttbar decays. Its potential impact on ttbar-related and ttH analyses will also be shown.

I am non-PhD student

Primary author

Prof. Hideki Okawa (高能所)

Co-authors

Michael Fenton (UC Irvine) Alexander Shmakov (UC Irvine) Ko-Yang Hsiao (National Tsing Hua) Shih-Chieh Hsu (University of Washington) Yuji Li (Fudan) Daniel Whiteson (UC Irvine) Pierre Baldi (UC Irvine)

Presentation materials

There are no materials yet.