Maurizio Pierini: Doing more with less: Deep Learning for Physics at the Large Hadron Collider
Channel: Oxford ML and Physics Seminars
Date: 2020-10-22

The CERN Large Hadron Collider produces more data than what the experiments can take. This creates practical problems with the computing infrastructure and conceptual problems with how the LHC ultimate goal (finding evidence of physics beyond the Standard Model) is pursued. With examples, we show how Deep Learning applications can help with this, reducing computing resource needs while extending the experimental sensitivity. We discuss technical challenges we face when deploying such a technology on unconventional equipments like the CMS experiment and the role of Deep Learning in the future High-Luminosity phase of the LHC.