Machine Learning Seminar
Speaker: Benjamin Nachman, Lawrence Berkeley National Laboratory and the University of California, Berkeley
Title: Machine Learning for Jet Physics at the Large Hadron Collider
Refreshments available at 2 pm.
Date: Mon, 26 Mar 2018, 2:15 pm – 3:15 pm
Location: 1400 BPS Bldg.
Modern machine learning (ML) has introduced a new and powerful toolkit to High Energy Physics. While only a small number of these techniques are currently used in practice, research and development centered around modern ML has exploded over the last year(s). I will highlight recent advances with a focus on jets: collimated sprays of particles resulting from quarks and gluons produced at high energy. Themselves defined by unsupervised learning algorithms, jets are a prime benchmark for state-of-the-art ML applications and innovations. For example, I will show how deep learning has been applied to jets for classification, regression, and generation. Recent advances have also added new capabilities to learn directly from data, without relying on simulation. These tools hold immense potential for extending the physics reach of the Large Hadron Collider and beyond.
Benjamin Nachman received his Ph.D. in Physics at Stanford University and is currently Owen Chamberlain postdoctoral fellow at Lawrence Berkeley National Laboratory and Simons-Berkeley research fellow at the Simons Institute for the Theory of Computing at the University of California, Berkeley.