Possible Σ(12−)\Sigma({1\over2}^-) under the Σ∗(1385)\Sigma^*(1385) peak in KΣ∗K\Sigma^* photoproduction

The LEPS collaboration has recently reported a measurement of the reaction $\gamma n\to K^+\Sigma^{*-}(1385)$ with linearly polarized photon beam at resonance region. The observed beam asymmetry is sizably negative at $E_\gamma=1.8-2.4 \mathrm{GeV}$, in contrast to the presented theoretical prediction. In this paper, we calculate this process in the framework of the effective Lagrangian approach. By including a newly proposed $\Sigma(J^P={1\over2}^-)$ state with mass around 1380~MeV, the experimental data for both $\gamma n$ and $\gamma p$ experiments can be well reproduced. It is found that the $\Sigma({1\over2}^-)$ and/or the contact term may play important role and deserve further investigation.Comment: modified version to be published at Phys. Rev.

Application of Rough Classification of Multi-objective Extension Group Decision-making under Uncertainty

On account of the problem of incomplete information system in classification of extension group decision-making, this paper studies attribution reduction with decision-making function based on the group interaction and individual preferences assembly for achieving the goal of rough classification of multi-objective extension group decision-making under uncertainty. Then, this paper describes the idea and operating processes of multi-objective extension classification model in order to provide decision-makers with more practical, easy to operate and objective classification. Finally, an example concerning practical problem is given to demonstrate the classification process. Combining by extension association and rough reduction, this method not only takes the advantages of dynamic classification in extension decision-making, but also achieves the elimination of redundant attributes, conducive to the promotion on the accuracy and the reliability of the classification results in multi-objective extension group decision-making. Keywords: extension group decision-making; matter-element analysis; extension association; rough set; attribution reductio

Insights into neutron star equation of state by machine learning

Due to its powerful capability and high efficiency in big data analysis, machine learning has been applied in various fields. We construct a neural network platform to constrain the behaviors of the equation of state of nuclear matter with respect to the properties of nuclear matter at saturation density and the properties of neutron stars. It is found that the neural network is able to give reasonable predictions of parameter space and provide new hints into the constraints of hadron interactions. As a specific example, we take the relativistic mean field approximation in a widely accepted Walecka-type model to illustrate the feasibility and efficiency of the platform. The results show that the neural network can indeed estimate the parameters of the model at a certain precision such that both the properties of nuclear matter around saturation density and global properties of neutron stars can be saturated. The optimization of the present modularly designed neural network and extension to other effective models are straightforward.Comment: 12 pages, 5 figures. Comments are welcom