Measurements of energy weighted angular correlations in electron positron annihilations at c.m. energies of 22 GeV and 34 GeV are presented.
We present data on energy-energy correlations (EEC) and their related asymmetry (AEEC) ine+e− annihilation in the centre of mass energy range 12<W≦46.8 GeV. The energy and angular dependence of the EEC in the central region is well described byOαs2 QCD plus a fragmentation term proportional to\({1 \mathord{\left/ {\vphantom {1 {\sqrt s }}} \right. \kern-\nulldelimiterspace} {\sqrt s }}\). BareO(α)s2 QCD reproduces our data for the large angle region of the AEEC. Nonperturbative effects for the latter are estimated with the help of fragmentation models. From various analyses using different approximations, we find that values for\(\Lambda _{\overline {MS} } \) in the range 0.1–0.3 GeV give a good description of the data. We also compare analytical calculations in QCD for the EEC in the back-to-back region to our data. The theoretical predictions describe well both the angular and energy dependence of the data in the back-to-back region.
Energy-energy-correlations (EEC) have been measured with the JADE detector at c.m. energies of 14 GeV, 22 GeV and in the region 29 GeV<Ecm<36 GeV. Corrected results are presented of EEC and their asymmetry, which can be directly compared to theoretical predictions. At 〈Ecm〉=34 GeV a comparison with second order QCD predictions yields good agreement for the string model fragmentation resulting in a value of the strong coupling constant αs=0.165±0.01 (stat.). The independent fragmentation models, which yield values of αs between 0.10 and 0.15 depending on the treatment of energy and momentum conservation and of the gluon splitting, do not provide a satisfactory description of the data over the full angular range.
We present high statistics measurements of the energy-energy correlation (EEC) and its related asymmetry (AEEC) ine+e− annihilation at a c.m. energy of 34.6 GeV. We find that the energy dependence as well as the large angle behaviour of the latter are well described by perturbative QCD calculations toOα(s2). Non-perturbative effects are estimated with the help of fragmentation models in which different jet topologies are separated using (ɛ, δ) cuts, and found to be small. The extracted values of\(\Lambda _{\overline {MS} }\) lie between 100 and 300 MeV.
An updated analysis using about 1.5 million events recorded at $\sqrt{s} = M_Z$ with the DELPHI detector in 1994 is presented. Eighteen infrared and collinear safe event shape observables are measured as a function of the polar angle of the thrust axis. The data are compared to theoretical calculations in ${\cal O} (\alpha_s^2)$ including the event orientation. A combined fit of $\alpha_s$ and of the renormalization scale $x_{\mu}$ in $\cal O(\alpha_s^2$) yields an excellent description of the high statistics data. The weighted average from 18 observables including quark mass effects and correlations is $\alpha_s(M_Z^2) = 0.1174 \pm 0.0026$. The final result, derived from the jet cone energy fraction, the observable with the smallest theoretical and experimental uncertainty, is $\alpha_s(M_Z^2) = 0.1180 \pm 0.0006 (exp.) \pm 0.0013 (hadr.) \pm 0.0008 (scale) \pm 0.0007 (mass)$. Further studies include an $\alpha_s$ determination using theoretical predictions in the next-to-leading log approximation (NLLA), matched NLLA and $\cal O(\alpha_s^2$) predictions as well as theoretically motivated optimized scale setting methods. The influence of higher order contributions was also investigated by using the method of Pad\'{e} approximants. Average $\alpha_s$ values derived from the different approaches are in good agreement.
We have studied the energy-energy correlation in e+e− annihilation into hadrons at √s =29 GeV using the Mark II detector at the SLAC storage ring PEP. We find to O(αs2) that αs=0.158±0.003±0.008 if hadronization is described by string fragmentation. Independent fragmentation schemes give αs=0.10–0.14, and give poor agreement with the data. A leading-log shower fragmentation model is found to describe the data well.