Machine Learning in High Energy Physics
Harrison B Prosper
2008 Invited Talk
Abstract
I begin with a brief discussion of the nature of high energy physics, and follow with a review of a few real-world examples of the application of machine learning methods in this field. I focus on the common, but difficult task, of extracting small signals masked by enormous backgrounds. The talk ends with a discussion of the computational challenges we expect to face in the very near future at the Large Hadron Collider and an enumeration of what my colleagues and I see as open questions.
Speaker
Harrison B Prosper
Harrison Prosper did his doctorate in particle physics from the University of
Manchester, England, in 1980 and, from 1982-1986, was a post-doctoral
fellow at the Rutherford Appleton Laboratory, but stationed at the
Institut Laue Langevin, Grenoble, France. In 1988, after a brief stint at
Virginia Tech, Blacksburg, he joined the Fermi National Accelerator
Laboratory as an Associate Scientist. In 1993,he joined the faculty at
Florida State University, became a full professor in 1998, was elected a
fellow of the American Physical Society in 2002, and became the Kirby W.
Kemper professor of physics in 2006. A principal interest of his is the
application of machine learning and Bayesian methods to particle physics
research.
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