Searches for New Heavy Resonances in Final States with Leptons and Photons in ATLAS and CMS

Searches for resonances in final states with leptons and photons have always been a powerful tool for discovery in high energy physics. We present here the latest results from the ATLAS and CMS experiments, based on up to 36.1 fb$^{-1}$ of 13 TeV proton-proton collisions produced at the Large Hadron Collider. Detailed results on single lepton, dilepton, diphoton and Z$\gamma$ resonances are included

Item selection by Latent Class-based methods

The evaluation of nursing homes is usually based on the administration of questionnaires made of a large number of polytomous items. In such a context, the Latent Class (LC) model represents a useful tool for clustering subjects in homogenous groups corresponding to different degrees of impairment of the health conditions. It is known that the performance of model-based clustering and the accuracy of the choice of the number of latent classes may be affected by the presence of irrelevant or noise variables. In this paper, we show the application of an item selection algorithm to real data collected within a project, named ULISSE, on the quality-of-life of elderly patients hosted in italian nursing homes. This algorithm, which is closely related to that proposed by Dean and Raftery in 2010, is aimed at finding the subset of items which provides the best clustering according to the Bayesian Information Criterion. At the same time, it allows us to select the optimal number of latent classes. Given the complexity of the ULISSE study, we perform a validation of the results by means of a sensitivity analysis to different specifications of the initial subset of items and of a resampling procedure

Search for the Standard Model Higgs boson in the H→ZZ→ℓ+ℓ−qqˉH \to ZZ \to \ell ^ + \ell ^ - q\bar q decay channel at CMS on 4.6 fb−1 of 7 TeV proton-proton collision data

A search for the standard model Higgs boson decaying to two Z bosons with subsequent decay to a final state containing two leptons and two quark-jets, $$H \to ZZ \to \ell ^ + \ell ^ - q\bar q$$ is presented. Results are based on data corresponding to an integrated luminosity of 4.6 fb−1 of proton-proton collisions at $$\sqrt s = 7$$ TeV and collected with the CMS detector at the CERN LHC. The analysis performance is strengthened against jet resolution effects thanks to an accurate jet energy calibration carried out on photon+jet events, and the use of a kinematic fit to the $$Z \to q\bar q$$ decay chain. Selections to discriminate between signal and background events are based on kinematic and topological quantities including the angular spin correlations of the decay products. Events are further classified for analysis according to the probability of the jets to originate from quarks of light or heavy flavor or from gluons. No evidence for a Higgs boson is found and upper limits on the Higgs boson production cross section are set in the range of masses between 200GeV and 600Ge

Performance-based wind risk assessment: computational tools for a building-component oriented vulnerability approach

The impact of extreme winds on industrial assets and the built environment is gaining increasing attention from stakeholders, including the corporate insurance industry. This has led to a progressively more in-depth study of building vulnerability and fragility to wind. Wind vulnerability models are used in probabilistic risk assessment, to relate a loss metric to an intensity measure of the natural event, in this case usually a gust or mean wind speed. In fact, vulnerability models can be integrated with wind hazard to provide an assessment of future losses due to extreme wind. Wind hazard on the other hand, can be quantified by associating a probability to the exceedance of each intensity level within a time interval which has been the objective of world- and regional-scale wind hazard studies. One approach often adopted for the probabilistic description of building vulnerability to wind, is the use of fragility functions, which provide the conditional probability that selected building components will exceed certain damage states, given wind intensity. In fact, in wind engineering literature, it is more common to find structural system- or component-level fragility functions, rather than wind vulnerability models for an entire building. In this context, models for assessing the vulnerability and fragility of structures to wind and their historical evolution over the last 50 years were investigated. On this basis it was possible to identify the fundamental characteristics in the assessment of the vulnerability of structures to wind. Among these, the importance of the relationships between the failures of different components of the structure emerged. Loss assessment based on component fragility requires some logical combination rules that define the building’s damage state given the damage state of each component, and the availability of a consequence model that provides the losses associated to each damage state. In state-of-the-art risk calculations, which are based on numerical simulation of a structure’s behavior during extreme wind scenarios, the interaction of component damage is intertwined with the computational procedure. Since simulation-based approaches are usually computationally demanding and case-specific, an approach for the composition of a fragility function for the entire structure, using available component fragilities, is developed and discussed in this thesis. This procedure also involves the development of a database containing a large number of recent and past vulnerability studies. The heterogeneity of models found in the literature also promoted a search for vulnerability function conversion methods. All these features have been integrated in the \textit{ExtReMe wind risk assESsment prototype Software, ERMESS}, an ad-hoc developed wind risk assessment tool for insurance applications, based on in-built or user-defined wind hazard data. Collecting a wide assortment of available wind vulnerability models and fragility functions, this software implements also the previously introduced alternative method for building-specific risk assessment based on existing component-level fragility functions and on a number of simplifying assumptions for their interactions. The applicability of ERMESS's output has been validated and, despite the simplifying assumptions, the procedure can yield evaluations that are comparable to those obtained via more rigorous building-level simulation-based methods, at least in the considered examples. The advantage of this approach lies in the fact that a database of building component fragility curves can be put to use for the development of new wind vulnerability models to cover building typologies not yet adequately covered by existing works and whose rigorous development is usually beyond the budget of portfolio-related industrial applications

A bivariate finite mixture growth model with selection

AbstractA model is proposed to analyze longitudinal data where two response variables are available, one of which is a binary indicator of selection and the other is continuous and observed only if the first is equal to 1. The model also accounts for individual covariates and may be considered as a bivariate finite mixture growth model as it is based on three submodels: (i) a probit model for the selection variable; (ii) a linear model for the continuous variable; and (iii) a multinomial logit model for the class membership. To suitably address endogeneity, the first two components rely on correlated errors as in a standard selection model. The proposed approach is applied to the analysis of the dynamics of household portfolio choices based on an unbalanced panel dataset of Italian households over the 1998–2014 period. For this dataset, we identify three latent classes of households with specific investment behaviors and we assess the effect of individual characteristics on households' portfolio choices. Our empirical findings also confirm the need to jointly model risky asset market participation and the conditional portfolio share to properly analyze investment behaviors over the life-cycle