6,920 research outputs found

    QCD corrections to e^+ e^- to J/\psi(\psi(2S))+\chi_{cJ} (J=0,1,2) at B Factories

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    We analytically calculate the cross sections of double charmonium production in e+eβˆ’β†’J/ψ(ψ(2S))Ο‡cJe^+ e^- \to J/\psi(\psi(2S))\chi_{cJ} (J=0,1,2) at next-to-leading order (NLO) in Ξ±s\alpha_s in nonrelativistic QCD, and confirm factorization of these processes. In contrast to Ο‡c0\chi_{c0} production, for which the NLO correction is large and positive, the NLO corrections for Ο‡c1,2\chi_{c1,2} production can be negative, resulting in decreased KK factors of 0.91 and 0.78 for J=1 and 2 respectively when ΞΌ=2mc\mu = 2 m_{c}. Consequently, the NLO QCD corrections markedly enlarge the difference between cross sections of Ο‡c0\chi_{c0} and Ο‡c1,2\chi_{c1,2}. This may explain why e+eβˆ’β†’J/ψ(ψ(2S))Ο‡c0e^+ e^- \to J/\psi(\psi(2S))\chi_{c0} but not e+eβˆ’β†’J/ψ(ψ(2S))Ο‡c1,2e^+ e^- \to J/\psi(\psi(2S))\chi_{c1,2} is observed experimentally. Moreover, for J/ψ(ψ(2S))Ο‡c1,2J/\psi(\psi(2S))\chi_{c1,2}, the NLO QCD corrections substantially reduce the ΞΌ\mu dependence and lead to predictions with small theoretical uncertainties.Comment: Version published in PRD, 6 pages, 3 figures, 2 tables, one reference adde

    J/psi (psi') production at the Tevatron and LHC at O(\alpha_s^4v^4) in nonrelativistic QCD

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    We present a complete evaluation for \jpsi(\psip) prompt production at the Tevatron and LHC at next-to-leading order in nonrelativistic QCD, including color-singlet, color-octet, and higher charmonia feeddown contributions. The short-distance coefficients of \pj at next-to-leading order are found to be larger than leading order by more than an order of magnitude but with a minus sign at high transverse momentum pTp_T. Two new linear combinations of color-octet matrix elements are obtained from the CDF data, and used to predict \jpsi production at the LHC, which agrees with the CMS data. The possibility of \sa dominance and the \jpsi polarization puzzle are also discussed.Comment: Version published in PRL, 4 pages, 4 figure

    Partitioning Clustering Based on Support Vector Ranking

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