SciPost Submission Page
Deep-Learning Jets with Uncertainties and More
by Sven Bollweg, Manuel Haussmann, Gregor Kasieczka, Michel Luchmann, Tilman Plehn, Jennifer Thompson
|As Contributors:||Manuel Haussmann · Michel Luchmann|
|Submitted by:||Luchmann, Michel|
|Submitted to:||SciPost Physics|
|Subject area:||High-Energy Physics - Phenomenology|
Bayesian neural networks allow us to keep track of uncertainties, for example in top tagging, by learning a tagger output together with an error band. We illustrate the main features of Bayesian versions of established deep-learning taggers. We show how they capture statistical uncertainties from finite training samples, systematics related to the jet energy scale, and stability issues through pile-up. Altogether, Bayesian networks offer many new handles to understand and control deep learning at the LHC without introducing a visible prior effect and without compromising the network performance.