arXiv subjects

Cristina-Andreea Alexe

Publications and source records attributed to Cristina-Andreea Alexe.

2 recordsLinked to original sources

arXiv2026

Recent electroweak measurements from the CMS experiment

Recent measurements of electroweak phenomena from the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider are summarized. The standard model of particle physics was tested through highly precise determinations of its key electroweak parameters and through measurements of electroweak processes in proton-proton collisions at unprecedented center-of-mass energies of up to $13.6 \, \mathrm{TeV}$. The performance of the CMS experiment establishes its key role in the study of electroweak physics, with many measurements performed either for the first time or with the best precision at a proton-proton collider, in some cases reaching or even surpassing the precision of legacy results from lepton colliders. Recent electroweak results from the CMS experiment include: measurements of the W and Z bosons production cross sections; high-precision measurements of the forward-backward asymmetry in Drell-Yan production and of the effective leptonic electroweak mixing angle; measurements of tau lepton properties and of multiboson production and vector boson scattering rates.

hep-ex↗

arXiv2026

Under-coverage in high-statistics counting experiments with finite MC samples

We consider the problem of setting confidence intervals on a parameter of interest from the maximum-likelihood fit of a physics model to a binned data set with a large number of bins, large event-counts per bin, and in the presence of systematic uncertainties modeled as nuisance parameters. We use the profile-likelihood ratio for statistical inference and focus on the case in which the model is determined from Monte Carlo simulated samples of finite size. We start by presenting a toy model in which the properties of widely used approximations of the profile-likelihood ratio in the asymptotic limit, which are commonly expected to hold in the high-statistics regime, are manifestly broken even if the numbers of events per bin in both the data and simulated samples are seemingly large enough to warrant their validity. We then move to the general setting to show how statistical uncertainties in the Monte Carlo predictions can affect the coverage of confidence intervals constructed in the asymptotic approximation always in the same direction, namely they lead to systematic under-coverage.

physics.data-an↗