Friday 27 April 2018 

Speaker: Ben Nachman (Lawrence Berkeley National Laboratory)


Title: "Machine Learning for Jet Physics at the Large Hadron Collider”

Abstract: 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.