Effective Field Theory and Machine Learning for Jet Physics

by Dr Aris Spourdalakis (University of Toronto)


In this talk a Soft Collinear Effective Theory (SCET) formalism for the calculation of Next to Leading Power (NLP) corrections will be presented along with a project concerned with the use of Machine Learning (ML) as anomaly detection in a QCD background. This SCET formalism has the advantage of being more easily applied to NLP corrections than the standard approach. In this talk, the application of this formalism for the massive Sudakov form factor, small qT Drell-Yan and Deep Inelastic Scattering (DIS) is presented. A comprehensive framework is setup both towards fully resumming large logarithms at NLP for these processes as well as the study of other processes in this framework. I will then transition to discussing the ML work which, just like EFT, the goal of improving our understanding of the QCD background as well as mitigating model dependence when searching for new physics.


Videoconference via