Correlated Systematic Uncertainties and Errors-on-Errors in Measurement Combinations: Methodology and Application to the 7-8 TeV ATLAS-CMS Top Quark Mass Combination
arxiv(2024)
Abstract
The Gamma Variance Model (GVM) is a statistical model that incorporates
uncertainties in the assignment of systematic errors (informally called
errors-on-errors). The model is of particular use in analyses that combine the
results of several measurements. In the past, combinations have been carried
out using two alternative approaches: the Best Linear Unbiased Estimator (BLUE)
method or what we will call the nuisance-parameter method. In this paper we
derive useful relations that allow one to connect the BLUE and
nuisance-parameter methods when the correlations induced by systematic
uncertainties are non-trivial (1, -1 or 0), and we generalise the
nuisance-parameter approach to include errors-on-errors. We then illustrate
some of the properties of the GVM by applying it to the 7-8 TeV ATLAS-CMS top
quark mass combination. We present results by considering the largest
systematic uncertainties as uncertain, one at a time, and we vary their
associated error-on-error parameters. This procedure is useful for identifying
the systematic uncertainties to which a combination is sensitive when they are
themselves uncertain. We also explore the hypothetical scenario of including an
outlier in the combination, which could become relevant for future
combinations, by artificially adding a fictitious measurement to it. This
example highlights a key feature of the GVM: its sensitivity to the internal
consistency of the input data.
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