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Optimal Statistical Inference in the Presence of Systematic Uncertainties Using Neural Network Optimization Based on Binned Poisson Likelihoods with Nuisance Parameters

Wunsch, Stefan; Jörger, Simon; Wolf, Roger; Quast, Günter

Abstract:

Data analysis in science, e.g., high-energy particle physics, is often subject to an intractable likelihood if the observables and observations span a high-dimensional input space. Typically the problem is solved by reducing the dimensionality using feature engineering and histograms, whereby the latter allows to build the likelihood using Poisson statistics. However, in the presence of systematic uncertainties represented by nuisance parameters in the likelihood, an optimal dimensionality reduction with a minimal loss of information about the parameters of interest is not known. This work presents a novel strategy to construct the dimensionality reduction with neural networks for feature engineering and a differential formulation of histograms so that the full workflow can be optimized with the result of the statistical inference, e.g., the variance of a parameter of interest, as objective. We discuss how this approach results in an estimate of the parameters of interest that is close to optimal and the applicability of the technique is demonstrated with a simple example based on pseudo-experiments and a more complex example from high-energy particle physics.


Verlagsausgabe §
DOI: 10.5445/IR/1000134467
Veröffentlicht am 28.06.2021
Originalveröffentlichung
DOI: 10.1007/s41781-020-00049-5
Scopus
Zitationen: 8
Dimensions
Zitationen: 8
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Experimentelle Teilchenphysik (ETP)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2021
Sprache Englisch
Identifikator ISSN: 2510-2036, 2510-2044
KITopen-ID: 1000134467
Erschienen in Computing and software for big science
Verlag Springer
Band 5
Heft 1
Seiten Art. Nr.: 4
Vorab online veröffentlicht am 12.01.2021
Nachgewiesen in Dimensions
Scopus
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