Machine Learning for 'New' New Physics Searches at the Energy Frontier - Julia L. Gonski (SLAC)

Abstract: The 2012 discovery of the Higgs boson with the Large Hadron Collider (LHC) was a groundbreaking achievement for high energy physics, and it remains today among the best compasses for where new physics can be hiding. Machine learning is a key tool for exploring the unknown, and can be incorporated throughout the collider data pipeline from acquisition and triggering to analysis. Adopting the latest and greatest technology in AI and "fast" ML can facilitate broad and novel searches for beyond the Standard Model particles, unlocking new ways to study the potential nature of dark matter or other hidden sector phenomena. 

This talk focuses on novel ATLAS searches in under-covered phase space, specifically long-lived particles and heavy resonances, that make use of the Higgs along with custom microelectronics and advanced AI/ML techniques such as anomaly detection. The High Luminosity LHC and studies for future colliders are both crucial for the longevity of these searches. These topics are discussed in the context of long-term planning for future experiments and the continued success of the field.