Speaker
Andrew Larkoski
(UCLA)
Description
Binary discrimination between well-defined signal and background datasets is a problem of fundamental importance in particle physics. In this talk, I present a first theoretical study of binary discrimination when the likelihood ratio is infrared and collinear safe, and derive expressions necessary for prediction of the ROC curve at next-to-leading order in the strong coupling. As an example of this framework, I apply it to H -> bb versus g -> bb discrimination and demonstrate that the description through NLO is required for qualitative understanding of corresponding results from machine learning studies.
Primary author
Andrew Larkoski
(UCLA)
