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ABSTRACT:
Final states that include hadronically decaying τ leptons are important in many analyses of the ATLAS experiment, such as measurements of Standard Model processes, Higgs boson searches, and searches for new physics phenomena. These analyses depend on robust τ reconstruction and excellent particle identification algorithms that provide suppression of backgrounds from jets, electrons and muons. In this talk, I will talk about the ATLAS single τ reconstruction algorithm, and introduce a novel boosted low pT boosted di-τ tagger. Besides, I will talk about the possibility of “Online Machine Learning”, implementing the machine learning model to the hardware trigger to improve the τ trigger performance.
在ATLAS实验中许多分析里，强衰变过程的τ粒子起到了非常重要的作用，包括标准模型的测量，希格斯波色子的探测和寻找超出标准模型等。这些分析都需要有一个效果良好的τ粒子鉴别技术，同时能压低来自其他可能的本底，像喷注，电子等。在这个报告中，我会介绍ATLAS中τ粒子的重建算法，和一个新的应用于低能量情况下的di-τ算法。另外，我还会介绍一下应用神经网络在触发器上辨别τ粒子的研究。
About the speaker:
Dr. Boping Chen is a postdoctoral fellow working on the ATLAS experiment at Tel Aviv University. He obtained his Ph.D. degree in December 2020 from Iowa State University. His thesis topic was searching for a heavy resonance decaying into a Standard Model Higgs boson and a photon. At Tel Aviv University, he is contributing to the ATLAS phase II τ trigger upgrade study, the validation for a novel low pT boosted di-τ tagger, and an analysis searching for Higgs boson decays into two a-bosons with subsequent decays to photons and hadronic τs.
陈博平，2020获得爱荷华州立大学的博士学位，博士课题是寻找衰变产物为希格斯粒子和光子的超出标准模型的重粒子。现在在特拉维夫大学担任博士后，参与数个分析，包括τ粒子触发器在ATLAS phase II的升级，应用于低能量情况下的di-τ算法的研究，和寻找希格斯粒子衰变到双光子加双τ粒子的分析。
Zoom Link: https://zoom.us/j/92378015184?pwd=TFExZ0hEY3FnUWpOSmdQbll2eVpCQT09
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