Handling uncertainties in background shapes: the discrete profiling method
read the original abstract
A common problem in data analysis is that the functional form, as well as the parameter values, of the underlying model which should describe a dataset is not known a priori. In these cases some extra uncertainty must be assigned to the extracted parameters of interest due to lack of exact knowledge of the functional form of the model. A method for assigning an appropriate error is presented. The method is based on considering the choice of functional form as a discrete nuisance parameter which is profiled in an analogous way to continuous nuisance parameters. The bias and coverage of this method are shown to be good when applied to a realistic example.
This paper has not been read by Pith yet.
Forward citations
Cited by 3 Pith papers
-
Search for single vector-like quark production in opposite-sign dilepton final states in proton-proton collisions at $\sqrt{s}$ = 13 TeV
No excess observed in search for vector-like top quark T to tH in opposite-sign dilepton final states; 95% CL upper limits on sigma times BR range from 2.0 pb at 600 GeV to 0.1 pb at 1000 GeV.
-
Gaussian Process Eigenmodes for Statistical and Systematic Uncertainties in Template Fits
Introduces Gaussian Process eigenmodes to unify statistical and systematic template uncertainties, replacing per-bin factors with truncated leading modes while containing Barlow-Beeston as a limiting case.
-
Constraints on anomalous Higgs boson couplings to vector bosons and fermions using the $\gamma\gamma$ final state in proton-proton collisions at $\sqrt{s}$ = 13 TeV
Updated constraints from the full Run 2 dataset show no evidence for anomalous Higgs couplings in gluon fusion, vector boson fusion, and associated production modes decaying to two photons.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.