About the rapidity and helicity distributions of the W bosons produced at LHC

$W$ bosons are produced at LHC from a forward-backward symmetric initial state. Their decay to a charged lepton and a neutrino has a strong spin analysing power. The combination of these effects results in characteristic distributions of the pseudorapidity of the leptons decaying from $W^+$ and $W^-$ of different helicity. This observation may open the possibility to measure precisely the $W^+$ and $W^-$ rapidity distributions for the two transverse polarisation states of $W$ bosons produced at small transverse momentum.Comment: 8 pages, 5 figure

Definition and calibration of the hadronic recoil in view of the measurement of the WW boson mass with the CMS experiment

Although the Standard Model of particle physics might appear as a complete theory, there are several theoretical problems and experimental observations which suggest that it is not the complete and definitive theory. For this reason, new physics is searched for both directly, and through precise measurements of standard model observables. A measurement of the mass of the $W$ boson with a relative precision of $10^{-4}$ is of primary interest because of the small uncertainty in its theoretical prediction, and because the LHC experiments have already collected several hundreds of millions of $W$ boson decays, which allow precise measurements of its properties. Such a measurement may represent a turning point in the world of particle physics, showing that the SM is inconsistent at this energy scale. In order to achieve such precision, it is requested to master the detector, the analysis, and the theoretical predictions at an unprecedented level. A study of the hadronic recoil is described in this thesis, resulting in a new and better experimental definition of this quantity. The power of the new definition is tested in terms of systematic uncertainties on the $W$ mass measurement, which are evaluated in a new and original way

Precision measurement of the top quark mass at threshold with the FCC-ee

The project concerns the study of the sensitivity in measuring the Top quark mass at threshold at the FCC-ee, using a Montecarlo analysis. In particular it has been developed within the FCC software framework, using a fast simulation. The work focused firstly on developing a b-tagging algorithm inside of the FCC fast simulation, managing to obtain results comparable with the ALEPH ones, and new results with a futuristic detector like ILD. Afterwards the selection of the tt events at energy close to the pair production threshold has been studied, obtaining a selection efficiency of 55 % and a background rejection at the level of 99.4 %. In the end, a list of points that can be achieved in the future, to complete the analysis, has been identified

Do Explanations Increase the Effectiveness of AI-Crowd Generated Fake News Warnings?

Social media platforms are increasingly deploying complex interventions to help users detect false news. Labeling false news using techniques that combine crowd-sourcing with artificial intelligence (AI) offers a promising way to inform users about potentially low-quality information without censoring content, but also can be hard for users to understand. In this study, we examine how users respond in their sharing intentions to information they are provided about a hypothetical human-AI hybrid system. We ask i) if these warnings increase discernment in social media sharing intentions and ii) if explaining how the labeling system works can boost the effectiveness of the warnings. To do so, we conduct a study (N=1473 Americans) in which participants indicated their likelihood of sharing content. Participants were randomly assigned to a control, a treatment where false content was labeled, or a treatment where the warning labels came with an explanation of how they were generated. We find clear evidence that both treatments increase sharing discernment, and directional evidence that explanations increase the warnings' effectiveness. Interestingly, we do not find that the explanations increase self-reported trust in the warning labels, although we do find some evidence that participants found the warnings with the explanations to be more informative. Together, these results have important implications for designing and deploying transparent misinformation warning labels, and AI-mediated systems more broadly