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Per-Object Systematics using Deep-Learned Calibration

by Gregor Kasieczka, Michel Luchmann, Florian Otterpohl, Tilman Plehn

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Submission summary

Authors (as registered SciPost users): Michel Luchmann · Tilman Plehn
Submission information
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
Academic field: Physics
Specialties:
  • High-Energy Physics - Phenomenology
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)


Reports on this Submission

Anonymous Report 1 on 2020-11-7 (Invited Report)

Report

The authors have satisfactorily addressed the points in my previous review.

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