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Towards Online Anomaly Detection for Particle Physics

Ben Nachman, LBNL
Monday, August 8, 2022 - 12:00pm
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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.

The A3D3 Seminar is a monthly lecture series that hosts scholars working across applied areas of artificial intelligence, such as hardware algorithm co-development, high energy physics, multi-messenger astrophysics,  and neuroscience. Our presenters come from all four domain fields and include occasional external speakers beyond the A3D3 science areas, governmental agencies and industry. The seminar will be recorded and published in YouTube. To receive future event update, subscribe here.