At the CERN Large Hadron Collider, protons are brought to collide hundreds of millions of times per second. The collision debris allows us to study the fundamental building blocks of the universe and look for hints of new forces and particles. The vast majority of the collision data are immediately discarded by a real-time event filtering system due to storage and computational limitations. While most of these data are uninterresting, signals of new physics might be inadvertendly thrown away in the process. The first stage of this event filtering system consists of hundreds of field-programmable gate arrays (FPGAs), tasked with rejecting over 98% of the proton collisions within a few microseconds. With the start of High Luminosity LHC in 2029, a more granular detector and more particles per collision will increase the event complexity significantly, and ultimately require the FPGA farm to process an amount of data comparable to 5% of the total internet traffic.
In this talk, I will discuss how real-time Machine Learning can be used to process and filter this enormous amount of data in order to improve physics acceptance, and how ML can be used to select data in ways never before performed at colliders.
Bio:
Thea Klaeboe Aarrestad is a Research Fellow at ETH Zürich and a member of the CMS Collaboration at CERN. Her research centers on how Machine Learning can be applied to particle physics problems, especially focusing on utilizing real-time ML for discovering new physics phenomena. She received her Ph.D. in Physics from the University of Zürich and spent two years as a Research Fellow at CERN before moving to Zürich as an SNSF Ambizione grantee.