Momentum dependence of the symmetry potential and its influence on nuclear reactions

A Skyrme-type momentum-dependent nucleon-nucleon force distinguishing isospin effect is parameterized and further implemented in the Lanzhou Quantum Molecular Dynamics (LQMD) model for the first time, which leads to a splitting of nucleon effective mass in nuclear matter. Based on the isospin- and momentum-dependent transport model, we investigate the influence of momentum-dependent symmetry potential on several isospin-sensitive observables in heavy-ion collisions. It is found that symmetry potentials with and without the momentum dependence but corresponding to the same density dependence of the symmetry energy result in different distributions of the observables. The mid-rapidity neutron/proton ratios at high transverse momenta and the excitation functions of the total $\pi^{-}/\pi^{+}$ and $K^{0}/K^{+}$ yields are particularly sensitive to the momentum dependence of the symmetry potential.Comment: 12 pages, 5 figure

Preequilibrium particle emissions and in-medium effects on the pion production in heavy-ion collisions

Within the framework of the Lanzhou quantum molecular dynamics (LQMD) transport model, pion dynamics in heavy-ion collisions near threshold energies and the emission of preequilibrium particles (nucleons and light complex fragments) have been investigated. A density, momentum and isospin dependent pion-nucleon potential based on the $\Delta$-hole model is implemented in the transport approach, which slightly leads to the increase of the $\pi^{-}/\pi^{+}$ ratio, but reduces the total pion yields. It is found that a bump structure of the $\pi^{-}/\pi^{+}$ ratio in the kinetic energy spectra appears at the pion energy close to the $\Delta$(1232) resonance region. The yield ratios of neutrons to protons from the squeeze-out particles perpendicular to the reaction plane are sensitive to the stiffness of nuclear symmetry energy, in particular at the high-momentum (kinetic energy) tails.Comment: 8 pages, 9 figures, submitted EPJA. arXiv admin note: text overlap with arXiv:1509.0479

Distributed Learning in Multi-Armed Bandit with Multiple Players

We formulate and study a decentralized multi-armed bandit (MAB) problem. There are M distributed players competing for N independent arms. Each arm, when played, offers i.i.d. reward according to a distribution with an unknown parameter. At each time, each player chooses one arm to play without exchanging observations or any information with other players. Players choosing the same arm collide, and, depending on the collision model, either no one receives reward or the colliding players share the reward in an arbitrary way. We show that the minimum system regret of the decentralized MAB grows with time at the same logarithmic order as in the centralized counterpart where players act collectively as a single entity by exchanging observations and making decisions jointly. A decentralized policy is constructed to achieve this optimal order while ensuring fairness among players and without assuming any pre-agreement or information exchange among players. Based on a Time Division Fair Sharing (TDFS) of the M best arms, the proposed policy is constructed and its order optimality is proven under a general reward model. Furthermore, the basic structure of the TDFS policy can be used with any order-optimal single-player policy to achieve order optimality in the decentralized setting. We also establish a lower bound on the system regret growth rate for a general class of decentralized polices, to which the proposed policy belongs. This problem finds potential applications in cognitive radio networks, multi-channel communication systems, multi-agent systems, web search and advertising, and social networks.Comment: 31 pages, 8 figures, revised paper submitted to IEEE Transactions on Signal Processing, April, 2010, the pre-agreement in the decentralized TDFS policy is eliminated to achieve a complete decentralization among player