Speaker
Christian Glaser
(Uppsala University)
Description
In this contribution I will review the use cases of uncertainty quantification with deep learning in high-energy astroparticle physics. Among other things, I will present the combination of neural networks with conditional normalizing flows to predict the Posterior for all quantities of interest. This Ansatz can be further expanded with the snowstorm method developed by the IceCube collaboration to include systematic uncertainties in the Posterior prediction by sampling from the systematic uncertainties during MC generation.
Author
Christian Glaser
(Uppsala University)