奇异粒子

  • 网络strange particle
奇异粒子奇异粒子
  1. B2羣与奇异粒子轻子型衰变

    The group b_2 and leptonic decays of strange particles

  2. 在研究RHIC及LHC能区奇异粒子时,考虑奇异夸克压低减弱机制能很好的解释实验现象。

    In the study of strange particles production at the RHIC and LHC energies , considering the reduction mechanism of strange quark suppression can well explain experimental phenomena .

  3. 本文用SU3和G2羣讨论了奇异粒子非轻子衰变的弱作用的变换性质。

    In this paper we have discussed the SU3 and G2 transformation properties of the weak interactions .

  4. 奇异粒子对核子磁矩的贡献

    Strange particles contribution to the magnetic moments of the nucleon

  5. 奇异粒子的性质及其产生和衰变

    The properties , production and decay of strange particle

  6. 奇异粒子非轻子衰变的选择定则

    Selection rules in nonleptonic decays of strange particles

  7. 奇异粒子的产生机制

    The production mechanism of strange particles

  8. 本文研究在夸克重组合模型的框架下高能重离子碰撞中奇异粒子的产生。

    We consider the strangeness production in relativistic heavy ion collisions in the quark recombination model .

  9. 奇异粒子的发现导致了核子守恒定理的推广。

    The discovery of strange particles lead to a generalization of the law of nucleon conservation .

  10. 所谈到的竞赛涉及一种称之为受激夸克的奇异设想粒子。

    The contest concerns an exotic hypothetical particle called an excited quark .

  11. 质子滴线附近奇异核粒子衰变研究

    Particle Decay Study of Exotic Nuclei near Proton Drip Line

  12. 碰撞强度成倍地增添了世界所等候的物理数据而且有可能加速搜罗希格斯粒子在内的奇异新粒子的发现。

    That collision intensity has led to double the data physicists around the world were hoping for , and could speed discovery of weird new particles including the Higgs .

  13. 由于在奇异核延迟粒子衰变中通常产生特征低能α粒子和质子,因此探测并鉴别这些粒子已成为研究新的核衰变机制和发现新核素的必要手段。

    Because of low energy α particle and protons are usually produced in unusual nuclei delay decay , so detection of these particles become necessary for research of new nuclear decay mechanism and discovery of new nuclei .

  14. 为了解决多奇异数重子两步拓扑重建效率低的问题,利用人工神经网络以较高的效率来一步完成对奇异粒子的鉴别。

    In order to solve the problem of low efficiency in two-step topological reconstruction of the multi-strange baryons it is planed to apply the artificial neural network to the identification of the baryons in one step with higher efficiency .