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Per-Object Systematics using Deep-Learned Calibration
by Gregor Kasieczka, Michel Luchmann, Florian Otterpohl, Tilman Plehn
This Submission thread is now published as
Submission summary
| Authors (as registered SciPost users): | Michel Luchmann · Tilman Plehn |
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| Preprint Link: | https://arxiv.org/abs/2003.11099v2 (pdf) |
| Date accepted: | 2020-11-18 |
| Date submitted: | 2020-11-03 14:37 |
| Submitted by: | Luchmann, Michel |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
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| Approach: | Phenomenological |
Abstract
We show how to treat systematic uncertainties using Bayesian deep networks for regression. First, we analyze how these networks separately trace statistical and systematic uncertainties on the momenta of boosted top quarks forming fat jets. Next, we propose a novel calibration procedure by training on labels and their error bars. Again, the network cleanly separates the different uncertainties. As a technical side effect, we show how Bayesian networks can be extended to describe non-Gaussian features.
List of changes
- section 2 was rewritten
- minor changes in other sections
Published as SciPost Phys. 9, 089 (2020)