203,107 research outputs found

    Study of charge-dependent azimuthal correlations using reaction-plane-dependent balance functions

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    STAR has recently reported charge-dependent azimuthal correlations that are sensitive to the charge separation effect in Au+Au collisions at sNN\sqrt{s_{\rm NN}} = 200 GeV. Qualitatively, these results agree with some of the theoretical predictions for local parity violation in heavy-ion collisions. However, a study using reaction-plane-dependent balance functions shows an alternative origin of this signal. The balance function, which measures the correlation between oppositely charged pairs, is sensitive to the mechanisms of charge formation and the subsequent relative diffusion of the balancing charges. The reaction-plane-dependent balance function measurements can be related to STAR's charge-dependent azimuthal correlations. We report reaction-plane-dependent balance functions for Au+Au collisions at sNN\sqrt{s_{\rm NN}} = 200, 62.4, 39, 11.5, and 7.7 GeV using the STAR detector. The model of Schlichting and Pratt incorporating local charge conservation and elliptic flow reproduces most of the three-particle azimuthal correlation results at 200 GeV. The experimental charge-dependent azimuthal charge correlations observed at 200 GeV can be explained in terms of local charge conservation and elliptic flow.Comment: Proceedings of the 22nd International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions (Annecy, France, 23-28 May 2011

    The Strong Decays of Orbitally Excited BsJB^{*}_{sJ} Mesons by Improved Bethe-Salpeter Method

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    We calculate the masses and the strong decays of orbitally excited states Bs0B_{s0}, Bs1B'_{s1}, Bs1B_{s1} and Bs2B_{s2} by the improved Bethe-Salpeter method. The predicted masses of Bs0B_{s0} and Bs1B'_{s1} are MBs0=5.723±0.280GeVM_{B_{s0}}=5.723\pm0.280 {\rm GeV}, MBs1=5.774±0.330GeVM_{B'_{s1}}=5.774\pm0.330 {\rm GeV}. We calculate the isospin symmetry violating decay processes Bs0BsπB_{s0}\to B_s \pi and Bs1BsπB'_{s1}\to B_s^* \pi through π0η\pi^0-\eta mixing and get small widths. Considering the uncertainties of the masses, for Bs0B_{s0} and Bs1B'_{s1}, we also calculate the OZI allowed decay channels: Bs0BKˉB_{s0}\to B\bar K and Bs1BKˉB'_{s1}\to B^*\bar K. For Bs1B_{s1} and Bs2B_{s2}, the OZI allowed decay channels Bs1BKˉB_{s1}\to B^{*}\bar K, Bs2BKˉB_{s2}\to B\bar K and Bs2BKˉB_{s2}\to B^{*}\bar K are studied. In all the decay channels, the reduction formula, PCAC relation and low energy theorem are used to estimate the decay widths. We also obtain the strong coupling constants GBs0BsπG_{B_{s0}B_s\pi}, GBs0BKˉG_{B_{s0}B\bar K}, GBs1BsπG_{B'_{s1}B_s^*\pi}, FBs1BsπF_{B'_{s1}B_s^*\pi}, GBs1BKˉG_{B'_{s1}B^*\bar K}, FBs1BKˉF_{B'_{s1}B^*\bar K}, GBs1BKˉG_{B_{s1}B^{*}\bar K}, FBs1BKˉF_{B_{s1}B^{*}\bar K}, GBs2BKˉG_{B_{s2}B\bar K} and GBs2BKˉG_{B_{s2}B^{*}\bar K}.Comment: 21 pages, 1 figure, 4 table

    Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis

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    This work aims at the goal whether the artificial intelligence can recognize phase transition without the prior human knowledge. If this becomes successful, it can be applied to, for instance, analyze data from quantum simulation of unsolved physical models. Toward this goal, we first need to apply the machine learning algorithm to well-understood models and see whether the outputs are consistent with our prior knowledge, which serves as the benchmark of this approach. In this work, we feed the compute with data generated by the classical Monte Carlo simulation for the XY model in frustrated triangular and union jack lattices, which has two order parameters and exhibits two phase transitions. We show that the outputs of the principle component analysis agree very well with our understanding of different orders in different phases, and the temperature dependences of the major components detect the nature and the locations of the phase transitions. Our work offers promise for using machine learning techniques to study sophisticated statistical models, and our results can be further improved by using principle component analysis with kernel tricks and the neural network method.Comment: 8 pages, 11 figure
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