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Institute
The inclusive transverse momentum (pT) distributions of primary charged particles are measured in the pseudo-rapidity range |η|<0.8 as a function of event centrality in Pb–Pb collisions at √sNN=2.76 TeV with ALICE at the LHC. The data are presented in the pT range 0.15<pT<50 GeV/c for nine centrality intervals from 70–80% to 0–5%. The results in Pb–Pb are presented in terms of the nuclear modification factor RAA using a pp reference spectrum measured at the same collision energy. We observe that the suppression of high-pT particles strongly depends on event centrality. The yield is most suppressed in central collisions (0–5%) with RAA≈0.13 at pT=6–7 GeV/c. Above pT=7 GeV/c, there is a significant rise in the nuclear modification factor, which reaches RAA≈0.4 for pT>30 GeV/c. In peripheral collisions (70–80%), only moderate suppression (RAA=0.6–0.7) and a weak pT dependence is observed. The measured nuclear modification factors are compared to other measurements and model calculations.
The ALICE Collaboration has made the first measurement at the LHC of J/ψ photoproduction in ultra-peripheral Pb–Pb collisions at sNN=2.76 TeV. The J/ψ is identified via its dimuon decay in the forward rapidity region with the muon spectrometer for events where the hadronic activity is required to be minimal. The analysis is based on an event sample corresponding to an integrated luminosity of about 55 μb−1. The cross section for coherent J/ψ production in the rapidity interval −3.6<y<−2.6 is measured to be dσJ/ψcoh/dy=1.00±0.18(stat)−0.26+0.24(syst) mb. The result is compared to theoretical models for coherent J/ψ production and found to be in good agreement with those models which include nuclear gluon shadowing.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.