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Discovering the Dark Universe with Artificial Intelligence

Shih-Chieh Hsu, University of Washington
Monday, November 21, 2022 - 4:00pm
PAA A-102

Compelling experimental evidence strongly supports searches for new particles predicted by the theories Beyond the Standard Model (BSM). Such searches are connected to fundamental questions among the highest priorities of particle physics, like dark matter, baryogenesis, and hierarchy problems. At the Large Hadron Collider (LHC), the ATLAS experiment is ideally suited for detecting heavy states like heavy Higgs and dark scalars that decay to particles with high transverse momentum (pT) to the beam. The FASER experiment is complementary to searches for Light and weakly-interacting BSM particles like dark photons, axion-like particles, and other Long-Lived Particles, produced with low pT. These particles may escape undetected along the beamline with significant energy and travel hundreds of meters without interacting or decaying. In this talk, I will present the state-of-the-art heavy Higgs and dark scalar searches in ATLAS and the status of the FASER physics analysis. Specifically, I will highlight how Artificial Intelligence plays a role to advance and expand the LHC physics program. This includes the improvement of on-going analyses through novel algorithm developments and new opportunities for discovery with accelerated Machine Learning, which can address big data challenges from the upcoming High-Luminosity LHC.

Video recording (UW only)

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