Apr 15 – 18, 2019
Europe/Zurich timezone
There is a live webcast for this event.

Uncertain Networks

Apr 16, 2019, 2:30 PM
500/1-001 - Main Auditorium (CERN)

500/1-001 - Main Auditorium


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Jennifer Thompson (ITP Heidelberg)


Machine learning methods are being increasingly and successfully applied to many different physics problems. However, currently uncertainties in machine learning methods are not modelled well, if at all. In this talk I will discuss how using Bayesian neural networks can give us a handle on uncertainties in machine learning. I will use tagging tops vs. QCD as an example of how these networks are competitive with other neural network taggers with the advantage of providing an event-by-event uncertainty on the classification. I will then further discuss how this uncertainty changes with experimental systematic effects, using pile-up and jet energy scale as examples.

Preferred contribution length 30 minutes

Primary authors

Gregor Kasieczka (Hamburg University (DE)) Jennifer Thompson (ITP Heidelberg) Tilman Plehn

Presentation materials