arXiv subjects

Enzo Canonero

Publications and source records attributed to Enzo Canonero.

4 recordsLinked to original sources

arXiv2026

Discovery Sensitivity for a Counting Experiment with Background Uncertainty

In Particle Physics, a search for a new signal process is often based on observing a Poisson-distributed number of events, whose mean contains contributions from background and, if it exists, the hypothesised signal. The discovery significance can be expressed as an equivalent number of standard deviations derived from the $p$-value of the background-only hypothesis. To characterise the experimental sensitivity, one may report the median, assuming a nominal signal strength, of the discovery significance. In this paper, approximate expressions for the median significance are derived both when the expected number of background events is known and when the background rate is uncertain but constrained by a Poisson control measurement. The formulae are based on a test statistic using the profile likelihood ratio, and the median significance is approximated using the Asimov data set. Higher-order asymptotic corrections, based on the Barndorff-Nielsen $r^\ast$ statistic, are incorporated for both the observed and expected discovery significance. The validity of the resulting expressions is compared with Monte Carlo results and with other formulae for expected significance often used in particle physics. The higher-order corrections are found to provide meaningful improvements at small event yields. The results are important for obtaining an accurate assessment of the sensitivity of a planned experiment and for the optimal choice of cuts that determine the expected numbers of signal and background events.

physics.data-an↗

arXiv2025

Correlated Systematic Uncertainties and Errors-on-Errors in Measurement Combinations: Methodology and Application to the 7-8 TeV ATLAS-CMS Top Quark Mass Combination

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.

hep-ex↗

arXiv2024

Higher-order asymptotic corrections and their application to the Gamma Variance Model

We present improved methods for calculating confidence intervals and $p$-values in situations where standard asymptotic approaches fail due to small sample sizes. We apply these techniques to a specific class of statistical model that can incorporate uncertainties in parameters that themselves represent uncertainties (informally, "errors on errors") called the Gamma Variance Model. This model contains fixed parameters, generically called $\varepsilon$, that represent the relative uncertainties in estimates of standard deviations of Gaussian distributed measurements. If the $\varepsilon$ parameters are small, one can construct confidence intervals and $p$-values using standard asymptotic methods. This is formally similar to the familiar situation of a large data sample, in which estimators for all adjustable parameters have Gaussian distributions. Here we address the important case where the $\varepsilon$ parameters are not small and as a consequence the asymptotic distributions do not represent a good approximation. We investigate improved test statistics based on the technology of higher-order asymptotics ($p^*$ approximation and Bartlett correction).

physics.data-an↗

arXiv2021

Publishing statistical models: Getting the most out of particle physics experiments

The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases -- including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits -- we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.

hep-ph↗