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.
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
Zoon ID: 92378015184