Abstract
A method is presented for the reduction of large sets of related uncertainty sources into strongly reduced representations which retain a suitable level of correlation information for use in many cases. The method provides a self-consistent means of determining whether a given analysis is sensitive to the loss of correlation information arising from the reduction procedure. The method is applied to the ATLAS Jet Energy Scale (JES) uncertainty, demonstrating that the set of 60+ independent sources can be reduced to form a representation constructed of 3 nuisance parameters. By forming a set of four such representations, it is shown that JES correlation information is retained or probed over the full parameter space to within an average of 1%. This procedure is expected to significantly reduce the computational requirements placed upon early ATLAS searches in the upcoming 2015 dataset while still providing suffcient performance and correlation structure to avoid changing the analysis results.
Highlights
Precision measurements of particle physics phenomena, as performed with the ATLAS detector [1] at the LHC [2], require accurate calibrations and a detailed understanding of physics objects
Improvements in the calibrations result in smaller uncertainties and better understanding of correlations between different phase space regions. This on the other hand results in a larger set of uncertainty sources, none of which is clearly dominant compared to the others
1 component applied to jets which are not fully contained in the calorimeters, which depends on pjTet, ηjet and the number of segments in the muon detectors aligned with the jet Fractional Jet Energy Scale (JES) uncertainty Fractional JES uncertainty
Summary
Precision measurements of particle physics phenomena, as performed with the ATLAS detector [1] at the LHC [2], require accurate calibrations and a detailed understanding of physics objects. Improvements in the calibrations result in smaller uncertainties and better understanding of correlations between different phase space regions. This on the other hand results in a larger set of uncertainty sources, none of which is clearly dominant compared to the others. The price of this precision is the increased computational complexity. Searches and measurements which are not sensitive to the details of the correlation loss can use one of the reduced representation This procedure allows a large fraction of analyses to use a simplified approach of determining the systematic uncertainties, without necessarily providing the optimal set of parameters for each individual analysis
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