Universidad Iberoamericana Ciudad de México

About: Universidad Iberoamericana Ciudad de México is a education organization based out in Mexico City, Mexico. It is known for research contribution in the topics: Large Hadron Collider & Standard Model. The organization has 2259 authors who have published 3981 publications receiving 109298 citations.

Measurements of Higgs boson properties in the diphoton decay channel in proton-proton collisions at √s=13 TeV

Abstract: Measurements of Higgs boson properties in the H → γγ decay channel are reported. The analysis is based on data collected by the CMS experiment in proton-proton collisions at $ \sqrt{s}=13 $ TeV during the 2016 LHC running period, corresponding to an integrated luminosity of 35.9 fb$^{−1}$. Allowing the Higgs mass to float, the measurement yields a signal strength relative to the standard model prediction of 1.18$_{− 0.14}^{+ 0.17}$ = 1.18$_{− 0.11}^{+ 0.12}$ (stat)$_{− 0.07}^{+ 0.09}$ (syst)$_{− 0.06}^{+ 0.07}$ (theo), which is largely insensitive to the exact Higgs mass around 125 GeV. Signal strengths associated with the different Higgs boson production mechanisms, couplings to bosons and fermions, and effective couplings to photons and gluons are also measured.

Studies of jet mass in dijet and W/Z + jet events

Abstract: Invariant mass spectra for jets reconstructed using the anti-kt and Cambridge-Aachen algorithms are studied for different jet "grooming" techniques in data corresponding to an integrated luminosity of 5 inverse femtobarns, recorded with the CMS detector in proton-proton collisions at the LHC at a center-of-mass energy of 7 TeV. Leading-order QCD predictions for inclusive dijet and W/Z+jet production combined with parton-shower Monte Carlo models are found to agree overall with the data, and the agreement improves with the implementation of jet grooming methods used to distinguish merged jets of large transverse momentum from softer QCD gluon radiation.

Direct measurement of the W production charge asymmetry in pp̄ collisions at s=1.96TeV

Abstract: We wish to thank RS Thorne and WJ Stirling for useful discussions on the theoretical predictionsWe thank the Fermilab staff and the technical staffs of the participating institutions for their vital contributions This work was supported by the US Department of Energy and National Science Foundation; the Italian Istituto Nazionale di Fisica Nucleare; the Ministry of Education, Culture, Sports, Science and Technology of Japan; the Natural Sciences and Engineering Research Council of Canada; the National Science Council of the Republic of China; the Swiss National Science Foundation; the AP Sloan Foundation; the Bundesministerium fur Bildung und Forschung, Germany; the Korean Science and Engineering Foundation and the Korean Research Foundation; the Science and Technology Facilities Council and the Royal Society, UK; the Institut National de Physique Nucleaire et Physique des Particules/CNRS; the Russian Foundation for Basic Research; the Comision Interministerial de Ciencia y Tecnologia, Spain; the European Community’s Human Potential Programme; the Slovak RD and the Academy of Finland

Are fit indices really fit to estimate the number of factors with categorical variables? Some cautionary findings via Monte Carlo simulation.

Abstract: An early step in the process of construct validation consists of establishing the fit of an unrestricted "exploratory" factorial model for a prespecified number of common factors. For this initial unrestricted model, researchers have often recommended and used fit indices to estimate the number of factors to retain. Despite the logical appeal of this approach, little is known about the actual accuracy of fit indices in the estimation of data dimensionality. The present study aimed to reduce this gap by systematically evaluating the performance of 4 commonly used fit indices-the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR)-in the estimation of the number of factors with categorical variables, and comparing it with what is arguably the current golden rule, Horn's (1965) parallel analysis. The results indicate that the CFI and TLI provide nearly identical estimations and are the most accurate fit indices, followed at a step below by the RMSEA, and then by the SRMR, which gives notably poor dimensionality estimates. Difficulties in establishing optimal cutoff values for the fit indices and the general superiority of parallel analysis, however, suggest that applied researchers are better served by complementing their theoretical considerations regarding dimensionality with the estimates provided by the latter method.