Particle Physics through the Lens of Machine Learning

Modern machine learning has had an outsized impact on many scientific fields, and particle physics is no exception.  What is special about particle physics, though, is the vast amount of theoretical and experimental knowledge that we already have about many problems in the field.  In this talk, I argue that we should fuse the revolutionary advances of "deep learning" with the time-tested strategies of "deep thinking" in physics.  To highlight this point, I show how machine learning applied to public data from the Large Hadron Collider has enabled new probes of quantum chromodynamics.