1. IE browser is NOT supported anymore. Please use Chrome, Firefox or Edge instead.
2. If you are a new user, please register to get an IHEP SSO account through https://login.ihep.ac.cn/registlight.jsp Any questions, please email us at helpdesk@ihep.ac.cn or call 88236855.
3. If you need to create a conference in the "Conferences, Workshops and Events" zone, please email us at helpdesk@ihep.ac.cn.
4. The max file size allowed for upload is 100 Mb.
PKU HEP Seminar and Workshop (北京大学高能物理组)

Invisible Higgs & trigger challenges on ATLAS: A discussion on Dark sector, Higgs boson, Trigger, and ML/AI on FPGA

by Prof. Tae Min Hong (University of Pittsburgh)

Online (Cloud)




STJU indico cross-reference: https://indico-tdli.sjtu.edu.cn/event/1690/

620 1653 9188 (Passcode: 247965)


Abstract - With more data coming from LHC collisions, detailed measurements of Higgs boson properties allow us to probe whether it communicates with the unknown and/or undiscovered sector beyond the Standard Model. One motivation is weakly interacting dark matter, which is invisible to the detecting apparatus, through a Higgs portal. I will discuss the latest ATLAS results of the search for Higgs decaying to invisible particles [2202.07953, 2109.00925]. I will also describe the technical challenges of triggering on such events using missing energy and/or jets, including some novel approaches to ML in real-time trigger systems [2104.03408, 2207.05602], including AI anomaly detection using autoencoders [2304.03836].


Bio - Tae Min Hong is Associate Professor of Physics and Astronomy at the University of Pittsburgh. He received his AB in Physics and Mathematics from Harvard University and MA and PhD in Physics from University of California at Santa Barbara. He is currently working on experimental particle physics using proton-proton collision data from the Large Hadron Collider with the ATLAS Collaboration. His research interests include connections of Higgs bosons to the dark matter sector, trigger algorithm development and operations, and implementations of machine learning and artificial intelligence on real-time trigger systems.