From Collision to Discovery: Machine Learning at the Large Hadron Collider

How do we solve the universe’s biggest secrets? At the Large Hadron Collider (LHC) - a 27-kilometer ring beneath the French-Swiss border - protons collide at nearly the speed of light, recreating conditions like just after the Big Bang. These collisions have led to groundbreaking insights, including the discovery of the Higgs boson in 2012, yet the greatest mysteries remain: what is the nature of dark matter and dark energy, which make up 95% of the universe’s energy but have never been observed directly.

Hunting for these elusive phenomena requires extraordinary algorithms and data analysis. The detectors at the LHC have access to data at an incredible rate of 60 terabytes per second - a perfect challenge for fast, high‑precision data analysis and machine learning (ML). In this talk, we’ll explore how ML powers countless stages of the scientific process: from real‑time event selection and particle reconstruction to the data analyses that lead to published discoveries.

Join us for a virtual visit to the LHC, where scientists push the limits of data and algorithms to shed light on the 95% of the universe that still lies in the dark.

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Dennis Noll is a postdoctoral researcher in physics at Stanford University. As a member of Prof. Nachman's research group, he uses the latest and most advanced computing techniques to tackle some of the most significant challenges in Particle Physics. Dennis's research focuses on the development and implementation of smart, fast, and reproducible physics analyses, leveraging machine learning, high performance computing, and graph-based computing workflows. He is an expert in Higgs boson research and is pioneering AI-driven methodologies to detect anomalies within the extensive datasets generated by the Large Hadron Collider (LHC) at CERN. Outside of his research, Dennis fosters collaboration and inclusion in the local postdoc community and optimizes his coffee consumption using Bayesian optimization.