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Understanding Event-Generation Networks via Uncertainties
by Marco Bellagente, Manuel Haußmann, Michel Luchmann, Tilman Plehn
This Submission thread is now published as
Submission summary
| Authors (as registered SciPost users): | Marco Bellagente · Tilman Plehn |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2104.04543v2 (pdf) |
| Date accepted: | Oct. 8, 2021 |
| Date submitted: | Oct. 4, 2021, 8:52 a.m. |
| Submitted by: | Bellagente, Marco |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| 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)