Date: 13 June 2024
Speaker: Liv Vage
Title: Graph neural nets and reinforcement learning for particle tracking at the LHC
Abstract: The Large Hadron Collider (LHC) at CERN in Switzerland produces data at a rate on the order of tens of petabytes per second, comparable with the data size and processing requirements of Google Cloud. Not all this data can be stored, and the main experiments at CERN have triggers that perform rapid processing to reduce the data size. One of the most time consuming parts of triggering is reconstructing particle tracks from where they left hits in the detector. LHC is set to undergo a series of upgrades, which will lead to a large increase in the number of simultaneously colliding particles. Reconstructing particle paths quickly becomes very challenging since connecting detector layer hits to form tracks becomes a highly combinatorial problem. This talk will discuss the machine learning potential for particle tracking, focusing on graph neural nets and reinforcement learning.
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