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Abstract:
After the first two successful runs of the CERN LHC, experimental collaborations, such as the CMS Collaboration, have collected and analyzed quadrillions of high energy proton-proton collisions and produced an extensive suite of physics results. Our understanding of the physics processes, particle interactions with the detector, and reconstruction algorithms has been significantly improved, with the help of modern tools such as machine learning and heterogeneous resources. Despite this, there are unknows within and outside the standard model that is interesting and worth investigation, such as the dark sector.
In this talk, I will quickly go through the CMS precison frontier results of electroweak W and Z measurements, and the technical tools we developed to improve reconstruction and computing. I will then focus on the planned DarkQuest experiment, summerizing the work we did to convert it from a proposal to a real experiment. Finally I will discuss the extensions of these studies, how we can apply these tools and knowledge to a broad range of experiments, and how far we can push these forward.
Bio:
Yongbin Feng is currently a postdoctoral research associate at Fermilab. He got the physics bachelor degree at University of Science and Technology of China in 2015, and the physics PhD at University of Maryland - College Park in 2020. He is interested in the dark sector searches with DarkQuest - a proton fixed-target experiment at Fermilab, and also electroweak precision measurements in the CMS experiment at CERN. For the technical research, he is focusing on applying ML algorithms to improve the reconstruction performance and accelerating the data processing with heterogeneous resouces and inference as-a-service.
Video link (Internal only):
https://vshare.sjtu.edu.cn/play/09d778b3-ad7c-4efa-8065-ae651420a2aa
Slides (Internal only):
https://jbox.sjtu.edu.cn/l/v1TTrP