There is a growing need for model-agnostic methods to complement existing searches for new fundamental particles and forces of nature. Powered by machine learning, new anomaly detection methods may allow us to be sensitive to unforeseen scenarios. However, if we don’t record anomalous events in the first place, there is no hope of finding them offline. I will introduce machine learning-based anomaly detection and then provide a perspective on how we can work towards online versions of these approaches.
Ben Nachman is a Staff Scientist in the Physics Division at LBNL where he is the group leader of the cross-cutting Machine Learning for Fundamental Physics. He was a Churchill Scholar at Cambridge University and then received his Ph.D. in Physics and Ph.D. minor in Statistics from Stanford University. After graduating, he was a Chamberlain Fellow in the Physics Division at Berkeley Lab. Nachman develops, adapts, and deploys machine learning algorithms to enhance data analysis in high energy physics.
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