Speaker
Andrew Larkoski
(Reed Collge)
Description
We study quark versus gluon discrimination systematically and present explicit calculations for jets on which up through three emissions are resolved. These explicit calculations enable determination of quantities central to machine learning, such as the likelihood, reducibility factors, and area under the ROC curve (AUC), to be calculated within a concrete approximation scheme. We prove many results regarding quark versus gluon discrimination including the reducibility factor for gluon jets with any number of resolved emissions, robust bounds on the AUC, and that the optimal observable for quark versus gluon discrimination is IRC safe.
Authors
Andrew Larkoski
(Reed Collge)
Eric Metodiev
(Massachusetts Institute of Technology)
