Title: Unveiling The Mysteries Of The Universe With Big Data From The Large Hadron Collider

Speaker: Sergei V. Gleyzer (University of Florida)

Abstract: The current generation of particle physics experiments, such as the Large Hadron Collider (LHC), are producing an order of magnitude more data than prior particle physics experiments. This trend is expected to continue with the upcoming upgrade to the LHC, the High-Luminosity Large Hadron Collider (HL-HLC), anticipated to start taking data in 2026. Traditional data processing and reduction methods are not adequate for extremely large volumes of data coming from these new experiments. Further challenges are posed by the rarity of the sought-after Standard Model signals, such as di-Higgs boson production, and the unknown properties of possible new physics processes, such as dark matter. Additional complexity at the HL-LHC arises from a significant increase in pile-up, or additional particle collisions of protons traveling in the same bunch, leading to more complex event signatures. A new approach to data analysis is required to address these challenges posed by the volumes of the data and greater event complexity. I will discuss how to build intelligent systems to extract knowledge from extremely large datasets, such as the one from the LHC. Many of the tools developed for high-energy physics are applicable to other fields. I will discuss the application of state-of-the-art data science methods, such as deep learning, to particle physics and focus on the solutions for the upcoming challenges of the high-luminosity environment of the HL-LHC. I will conclude by presenting new opportunities in the field of particle physics enabled by data science.