SciPost Submission Page
Understanding Event-Generation Networks via Uncertainties
by Marco Bellagente, Manuel Haußmann, Michel Luchmann, Tilman Plehn
This Submission thread is now published as SciPost Phys. 13, 003 (2022)
Submission summary
As Contributors: | Marco Bellagente · Tilman Plehn |
Arxiv Link: | https://arxiv.org/abs/2104.04543v2 (pdf) |
Date accepted: | 2021-10-08 |
Date submitted: | 2021-10-04 08:52 |
Submitted by: | Bellagente, Marco |
Submitted to: | SciPost Physics |
Academic field: | Physics |
Specialties: |
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Approaches: | Computational, Phenomenological |
Abstract
Following the growing success of generative neural networks in LHC simulations, the crucial question is how to control the networks and assign uncertainties to their event output. We show how Bayesian normalizing flow or invertible networks capture uncertainties from the training and turn them into an uncertainty on the event weight. Fundamentally, the interplay between density and uncertainty estimates indicates that these networks learn functions in analogy to parameter fits rather than binned event counts.
Published as SciPost Phys. 13, 003 (2022)
Submission & Refereeing History
Published as SciPost Phys. 13, 003 (2022)
Submission 2104.04543v1 on 3 May 2021