Speaker
Benedikt Schosser
Description
Latent representations are an important theme in modern machine learning. While information geometry provides a framework to analyze their structure, it also offers new insight into the physics learned by jet-tagging networks. We apply these methods to binary quark-gluon classification and three-fold fat-jet tagging and relate the learned latent representation to characteristic features of QCD radiation and heavy-particle decays. We show how information-geometric observables identify the relevant physics encoded by the network and connect the classifier’s decisions to established jet observables.
Authors
Benedikt Schosser
Björn Malte Schäfer
Rebecca Maria Wolfgramm-Kuntz
(Astronomisches Rechen-Institut, Zentrum für Astronomie, Universität Heidelberg)
Sophia Vent
Tilman Plehn