On non-parametric tests for discovery and limit setting in one and multiple 
dimensions

Shtembari, Lolian

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On non-parametric tests for discovery and limit setting in one and multiple dimensions

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Shtembari, Lolian
Max Planck Institute for Physics, Max Planck Society and Cooperation Partners;

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https://mediatum.ub.tum.de/?id=1707763
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Citation

Shtembari, L. (2023). On non-parametric tests for discovery and limit setting in one and multiple dimensions. PhD Thesis, TUM, München.

Cite as: https://hdl.handle.net/21.11116/0000-000F-127A-4

Abstract

Spacings between ordered data are used to develop unbinned non-parametric Goodness of Fit (GoF) tests to detect unknown signals against known backgrounds or set limits on proposed signals contaminated by unknown backgrounds. I also extend GoF tests to multivariate samples, using a multivariate probability integral transformation, carried out either analytically or numerically using a Normalizing Flow, ultimately reducing the problem to a single or multiple univariate uniformity tests.