为了从由两个观测量定义的选定事件样本或中心度中重建碰撞参数分布，我们扩展了Das等人[Phys. Rev. C 97, 014905 (2018)]、Rogly等人[Phys. Rev. C 98, 024902 (2018)]和Frankland等人[Phys. Rev. C 104, 034609 (2021)]提出的方法。基于对涨落机制的深入研究，我们发现内在涨落主要产生于重离子碰撞非平衡过程中初始化和核子-核子碰撞的微观随机性，这导致观测量相对于碰撞参数以高斯形式涨落。在本工作中，我们同时使用带电粒子多重性和轻带电粒子总横向动量，基于贝叶斯方法模型无关地重建选定事件或中心度的碰撞参数分布。对于使用两个观测量进行中心度分类，我们提出使用$K$-means聚类方法（一种无监督机器学习算法），当给定类别数时，该方法可以自动对事件进行分类。此外，从两个观测量的数据重建的碰撞参数分布可用于学习不同中心度下多重性与横向动量之间的相关性，这可能有助于理解碎裂机制。
英文摘要：
To reconstruct the impact parameter distributions from the selected events sample or centrality, which is defined by two-observables, at intermediate energy heavy ion collisions, we extend the approach proposed by Das \textit{et al.} [Phys. Rev. C 97, 014905 (2018)], Rogly \textit{et al.} [Phys. Rev. C 98, 024902 (2018)], and Frankland \textit{et al.} [Phys. Rev. C 104, 034609 (2021)]. Based on deep investigations of the fluctuation mechanism, we found that the intrinsic fluctuations are mainly generated in the microscopic stochasticity of initialization and nucleon-nucleon collisions in the nonequilibrium process of heavy ion collisions, and this leads the observables to fluctuate with respect to impact parameter in a Gaussian form. In this work, the multiplicity of the charged particles and the total transverse momentum of the light charged particles are used simultaneously to model-independently reconstruct the impact parameter distributions for selected events or centrality based on the Bayesian method. For sorting the centrality with two observables, we propose to use the $K$-means clustering method (an unsupervised machine learning algorithm), which can automatically sort events when the class number is given. Furthermore, the reconstructed impact parameter distributions from data of the two observables can be used to learn the correlation between multiplicity and transverse momentum at different centralities, which may be useful for understanding the fragmentation mechanism.
Bayesian Inference of Heavy-Quark Dissipation and Jet Transport Parameters from D-Meson observables in heavy-ion collisions at the LHC energies
School of Mathematics and Physics, China University of Geosciences（中国地质大学数学与物理学院）；Key Laboratory of Quark & Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University（华中师范大学夸克与轻子物理教育部重点实验室及粒子物理研究所）；Helmholtz Research Academy Hesse for FAIR (HFHF), GSI Helmholtz Center for Heavy Ion Physics（黑森FAIR亥姆霍兹研究学院（GSI重离子物理中心））；Institut für Theoretische Physik, Johann Wolfgang Goethe-Universität（约翰·沃尔夫冈·歌德大学理论物理研究所）
Effects of retarded electrical fields on observables sensitive to the high-density behavior of nuclear symmetry energy in heavy-ion collisions at intermediate energies
Bayesian inference of dense matter EOS encapsulating a first-order hadron-quark phase transition from observables of canonical neutron stars
【目的】利用LIGO/VIRGO、NICER和Chandra等机构报道的典型NS半径数据，推导描述强子-夸克一阶相变的致密丰中子物质状态方程的后验概率分布函数（PDF）和9个参数的相关性。我们还推断了1.4 M $_{\odot}$ NS中的夸克物质（QM）质量分数及其半径，并预测了它们在更大质量NS中的值。[方法]在贝叶斯统计框架下，采用马尔可夫链蒙特卡罗抽样方法，利用元模型生成强子和量子态观测系统。一个明确的同位旋相关的参数状态方程的$npeμ$物质在NS在$β$平衡连接通过麦克斯韦建设的QM状态方程所描述的恒定声速（CSS）模型的Alford，汉和Prakash。[结果]（1）在68%置信水平下，强子-夸克跃迁密度$ρ_t/ρ_0$和相对能量密度跃迁$\De\ep/\ep_t$的最可几值分别为$ρ_t/ρ_0=1.6^{+1.2}_{-0.4}$和$\De\ep/\ep_t=0.4^{+0.20}_{-0.15}$。相应的概率分布的QM分数在1.4 M $_{\odot}$ NS峰值约0.9在10公里的球体。与$ρ_t$和$\De\ep/\ep_t$的PDF强相关，QM声速平方$\cQMsq/c^2$的PDF在$0.95^{+0.05}_{-0.35}$处达到峰值，并且小于1/3的总概率非常小。(2)强子的概率密度函数与量子态方程参数之间的相关性很弱。【结论】现有的天体物理数…
Neural network enhanced Bayesian global analysis of relativistic heavy ion collisions
Incubator of Scientific Excellence - Centre for Simulations of Superdense Fluids, University of Wrocław（弗罗茨瓦夫大学超密流体模拟中心卓越科学孵化器）；University of Jyväskylä（于韦斯屈莱大学）；Helsinki Institute of Physics（赫尔辛基物理研究所）；Vanderbilt University（范德堡大学）
