University of Ioannina

About: University of Ioannina is a education organization based out in Ioannina, Greece. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 7654 authors who have published 20594 publications receiving 671560 citations. The organization is also known as: Panepistimio Ioanninon.

Comparison of the legacy and gold type Ia supernovae dataset constraints on dark energy models

Abstract: We have performed a comparative analysis of three recent and reliable SnIa datasets available in the literature: the Full Gold (FG) dataset (157 data points 0

Constraints on the spin-parity and anomalous HVV couplings of the Higgs boson in proton collisions at 7 and 8 TeV

Abstract: The study of the spin-parity and tensor structure of the interactions of the recently discovered Higgs boson is performed using the H to ZZ, Z gamma*, gamma* gamma* to 4 l, H to WW to l nu l nu, and H to gamma gamma decay modes. The full dataset recorded by the CMS experiment during the LHC Run 1 is used, corresponding to an integrated luminosity of up to 5.1 inverse femtobarns at a center-of-mass energy of 7 TeV and up to 19.7 inverse femtobarns at 8 TeV. A wide range of spin-two models is excluded at a 99% confidence level or higher, or at a 99.87% confidence level for the minimal gravity-like couplings, regardless of whether assumptions are made on the production mechanism. Any mixed-parity spin-one state is excluded in the ZZ and WW modes at a greater than 99.999% confidence level. Under the hypothesis that the resonance is a spin-zero boson, the tensor structure of the interactions of the Higgs boson with two vector bosons ZZ, Z gamma, gamma gamma, and WW is investigated and limits on eleven anomalous contributions are set. Tighter constraints on anomalous HVV interactions are obtained by combining the HZZ and HWW measurements. All observations are consistent with the expectations for the standard model Higgs boson with the quantum numbers J[PC]=0[++].

Evaluation of inconsistency in networks of interventions

Abstract: Background The assumption of consistency, defined as agreement between direct and indirect sources of evidence, underlies the increasingly popular method of network meta-analysis. No evidence exists so far regarding the extent of inconsistency in full networks of interventions or the factors that control its statistical detection. Methods In this paper we assess the prevalence of inconsistency from data of 40 published networks of interventions involving 303 loops of evidence. Inconsistency is evaluated in each loop by contrasting direct and indirect estimates and by employing an omnibus test of consistency for the entire network. We explore whether different effect measures for dichotomous outcomes are associated with differences in inconsistency, and evaluate whether different ways to estimate heterogeneity affect the magnitude and detection of inconsistency. Results Inconsistency was detected in from 2% to 9% of the tested loops, depending on the effect measure and heterogeneity estimation method. Loops that included comparisons informed by a single study were more likely to show inconsistency. About one-eighth of the networks were found to be inconsistent. The proportions of inconsistent loops do not materially change when different effect measures are used. Important heterogeneity or the overestimation of heterogeneity was associated with a small decrease in the prevalence of statistical inconsistency. Conclusions The study suggests that changing the effect measure might improve statistical consistency, and that an analysis of sensitivity to the assumptions and an estimator of heterogeneity might be needed before reaching a conclusion about the absence of statistical inconsistency, particularly in networks with few studies.