Accelerated Real-TimE Machine learning for Investigating the higgs Sector

Objective

The discovery of the Higgs boson marked a milestone in particle physics, but crucial questions about how the Higgs boson interacts with itself and with heavy bosons remain unanswered. These interactions hold the key to understanding fundamental aspects of our universe, from particle mass generation to the nature of the cosmic phase transition in the early universe. Current measurements at the Large Hadron Collider (LHC) can only place weak constraints on these interactions due to significant limitations in our ability to identify and record the relevant collision events in real-time. This project introduces a revolutionary approach to overcome these limitations by developing advanced machine learning techniques for real-time event selection in particle physics experiments. The current approach discards up 50\% of potentially valuable collision events involving hadronic decays of the Higgs boson. By implementing sophisticated neural networks that can process collision data fast, this project aims to double the detection efficiency for these crucial events. The proposed system will operate at different stages of the data-taking process, from specialized hardware (FPGAs) to traditional computing infrastructure. The work will be carried out at the ATLAS experiment at CERN and will significantly enhance our ability to measure the Higgs boson's self-coupling, either leading to the discovery of new physics or placing stronger constraints on theories beyond the Standard Model. The PI brings extensive expertise in both Higgs physics and machine learning applications at the trigger level. An ERC Consolidator Grant will enable the formation of an independent research team with the necessary expertise to achieve these ambitious goals and advance our understanding of nature's fundamental interactions.