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Register a new userA more exact solution for incorporating multiplicative systematic uncertainties in branching ratio limits
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K. Stenson
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A method to include multiplicative systematic uncertainties into branching ratio limits was proposed by M. Convery. That solution used approximations which are not necessarily valid. This note provides a solution without approximations and compares the results.
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Recent statistical evaluations for High-Energy Physics measurements, in particular those at the Large Hadron Collider, require careful evaluation of many sources of systematic uncertainties at the same time. While the fundamental aspects of the statistical treatment are now consolidated, both using a frequentist or a Bayesian approach, the management of many sources of uncertainties and their corresponding nuisance parameters in analyses that combine multiple control regions and decay channels, in practice, may pose challenging implementation issues, that make the analysis infrastructure complex and hard to manage, eventually resulting in simplifications in the treatment of systematics, and in limitations to the result interpretation. Typical cases will be discussed, having in mind the most popular implementation tool, RooStats, with possible ideas about improving the management of such cases in future software implementations.
A maximum likelihood method is used to deal with the combined estimation of multi-measurements of a branching ratio, where each result can be presented as an upper limit. The joint likelihood function is constructed using observed spectra of all measurements and the combined estimate of the branching ratio is obtained by maximizing the joint likelihood function. The Bayesian credible interval, or upper limit of the combined branching ratio, is given in cases both with and without inclusion of systematic error.
Several experiments in high-energy physics and astrophysics can be treated as on/off measurements, where an observation potentially containing a new source or effect (on measurement) is contrasted with a background-only observation free of the effect (off measurement). In counting experiments, the significance of the new source or effect can be estimated with a widely-used formula from [LiMa], which assumes that both measurements are Poisson random variables. In this paper we study three other cases: i) the ideal case where the background measurement has no uncertainty, which can be used to study the maximum sensitivity that an instrument can achieve, ii) the case where the background estimate $b$ in the off measurement has an additional systematic uncertainty, and iii) the case where $b$ is a Gaussian random variable instead of a Poisson random variable. The latter case applies when $b$ comes from a model fitted on archival or ancillary data, or from the interpolation of a function fitted on data surrounding the candidate new source/effect. Practitioners typically use in this case a formula which is only valid when $b$ is large and when its uncertainty is very small, while we derive a general formula that can be applied in all regimes. We also develop simple methods that can be used to assess how much an estimate of significance is sensitive to systematic uncertainties on the efficiency or on the background. Examples of applications include the detection of short Gamma-Ray Bursts and of new X-ray or $gamma$-ray sources.
Geant4Reweight is an open-source C++ framework that allows users to 1) weight tracks produced by the GEANT4 particle transport Monte Carlo simulation according to hadron interaction cross section variations and 2) estimate uncertainties in GEANT4 interaction models by comparing the simulations hadron interaction cross section predictions to data. The ability to weight hadron transport as simulated by GEANT4 is crucial to the propagation of systematic uncertainties related to secondary hadronic interactions in current and upcoming neutrino oscillation experiments, including MicroBooNE, NOvA, and DUNE, as well as hadron test beam experiments such as ProtoDUNE. We provide motivation for weighting hadron tracks in GEANT4 in the context of systematic uncertainty propagation, a description of GEANT4s transport simulation technique, and a description of our weighting technique and fitting framework in the momentum range 0--10 GeV/c, which is typical for the hadrons produced by neutrino interactions in these experiments.
The suitability of a mathematical-model Y = f({Xi}) in serving a purpose whatsoever (should be preset by the function f specific input-to-output variation-rates, i.e.) can be judged beforehand. We thus evaluate here the two apparently similar models: YA = fA(SRi,WRi) = (SRi/WRi) and: YD = fd(SRi,WRi) = ([SRi,WRi] - 1) = (YA - 1), with SRi and WRi representing certain measurable-variables (e.g. the sample S and the working-lab-reference W specific ith-isotopic-abundance-ratios, respectively, for a case as the isotope ratio mass spectrometry (IRMS)). The idea is to ascertain whether fD should represent a better model than fA, specifically, for the well-known IRMS evaluation. The study clarifies that fA and fD should really represent different model-families. For example, the possible variation, eA, of an absolute estimate as the yA (and/ or the risk of running a machine on the basis of the measurement-model fA) should be dictated by the possible Ri-measurement-variations (u_S and u_W) only: eA = (u_S + u_W); i.e., at worst: eA = 2ui. However, the variation, eD, of the corresponding differential (i.e. YD) estimate yd should largely be decided by SRi and WRi values: ed = 2(|m_i |x u_i) = (|m_i | x eA); with: mi = (SRi/[SRi - WRi]). Thus, any IRMS measurement (i.e. for which |SRi - WRi| is nearly zero is a requirement) should signify that |mi| tends to infinity. Clearly, yD should be less accurate than yA, and/ or even turn out to be highly erroneous (eD tends to infinity). Nevertheless, the evaluation as the absolute yA, and hence as the sample isotopic ratio Sri, is shown to be equivalent to our previously reported finding that the conversion of a D-estimate (here, yD) into Sri should help to improve the achievable output-accuracy and -comparability.
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