Computes two-loop jet functions for N=4,5,6 projected energy correlators enabling NNLL collinear resummation matched to NLO in e+e- and Higgs-to-gluons processes, with non-perturbative corrections from two universal soft matrix elements.
$O(\alpha_s^2)$ Contributions to the asymmetric fragmentation function in $e^+e^-$ annihilation
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abstract
The order \alpha_s^2 contributions to the coefficient functions corresponding to the asymmetric fragmentation function $F_A(x,Q^2)$ in $e^+e^-$ annihilation are calculated. From this calculation we infer that the order $(\alpha_s/4\pi)^2$ correction to the flavour asymmetry sum rule is non vanishing and amounts to $-12\beta_0C_F\zeta(3)$. We also study the effect of the higher order QCD corrections on $F_A(x,Q^2)$ and compare them with the OPAL data. The latter put a strong constraint on the valence part of the fragmentation densities $D_q^H(x,\mu^2)$.
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Non-singlet fragmentation functions of pions and kaons are determined at NNLO QCD from charge asymmetry measurements in e+e- annihilation and SIDIS, yielding a scaling index of 0.7 and strangeness suppression of 0.5.
First NNLO global extraction of Lambda hyperon fragmentation functions from SIA and SIDIS data via neural-network parametrization.
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Projected Energy Correlators: Two-Loop Jet Functions and NNLL Resummation
Computes two-loop jet functions for N=4,5,6 projected energy correlators enabling NNLL collinear resummation matched to NLO in e+e- and Higgs-to-gluons processes, with non-perturbative corrections from two universal soft matrix elements.
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Determination of Fragmentation Functions from Charge Asymmetries in Hadron Production
Non-singlet fragmentation functions of pions and kaons are determined at NNLO QCD from charge asymmetry measurements in e+e- annihilation and SIDIS, yielding a scaling index of 0.7 and strangeness suppression of 0.5.
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First next-to-next-to-leading-order extraction of fragmentation functions for Lambda hyperons
First NNLO global extraction of Lambda hyperon fragmentation functions from SIA and SIDIS data via neural-network parametrization.