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Institute
The ALICE Collaboration has measured inclusive J/ψ production in pp collisions at a center-of-mass energy √s=2.76 TeV at the LHC. The results presented in this Letter refer to the rapidity ranges |y|<0.9 and 2.5<y<4 and have been obtained by measuring the electron and muon pair decay channels, respectively. The integrated luminosities for the two channels are Linte=1.1 nb−1 and Lintμ=19.9 nb−1, and the corresponding signal statistics are NJ/ψe+e−=59±14 and NJ/ψμ+μ−=1364±53. We present dσJ/ψ/dy for the two rapidity regions under study and, for the forward-y range, d2σJ/ψ/dydpt in the transverse momentum domain 0<pt<8 GeV/c. The results are compared with previously published results at s=7 TeV and with theoretical calculations.
A measurement of the multi-strange Ξ− and Ω− baryons and their antiparticles by the ALICE experiment at the CERN Large Hadron Collider (LHC) is presented for inelastic proton–proton collisions at a centre-of-mass energy of 7 TeV. The transverse momentum (pT) distributions were studied at mid-rapidity (|y|<0.5) in the range of 0.6<pT<8.5 GeV/c for Ξ− and Ξ¯+ baryons, and in the range of 0.8<pT<5 GeV/c for Ω− and Ω¯+. Baryons and antibaryons were measured as separate particles and we find that the baryon to antibaryon ratio of both particle species is consistent with unity over the entire range of the measurement. The statistical precision of the current data has allowed us to measure a difference between the mean pT of Ξ− (Ξ¯+) and Ω− (Ω¯+). Particle yields, mean pT, and the spectra in the intermediate pT range are not well described by the PYTHIA Perugia 2011 tune Monte Carlo event generator, which has been tuned to reproduce the early LHC data. The discrepancy is largest for Ω− (Ω¯+). This PYTHIA tune approaches the pT spectra of Ξ− and Ξ¯+ baryons below pT<0.85 GeV/c and describes the Ξ− and Ξ¯+ spectra above pT>6.0 GeV/c. We also illustrate the difference between the experimental data and model by comparing the corresponding ratios of (Ω−+Ω¯+)/(Ξ−+Ξ¯+) as a function of transverse mass.
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.