Bayesian analyses on both isoscalar and isovector nuclear interaction parameters are carried out based on the Korea-IBS-Daegu-SKKU (KIDS) model under the constraints of nuclear structure data of $^{208}$Pb and $^{120}$Sn. Under the constraint of the neutron-skin thickness, it is found that incorporating the curvature parameter $K_{sym}$ of nuclear symmetry energy as an independent variable significantly broadens the posterior probability distribution function (PDF) of the slope parameter $L$, and affects the related correlations. Typically, the anticorrelation between $L$ and the symmetry energy at saturation density disappears, while a positive correlation between $L$ and $K_{sym}$ is observed. Under the constraint of the isoscalar giant monopole resonance (ISGMR), incorporating the skewness parameter as an independent variable also significantly broadens the posterior PDF of the nuclear matter incompressibility $K_0$. Even with the broad uncertainties of higher-order parameters of the equation of state (EOS), robust constraints of $L<90$ MeV and $K_0<270$ MeV are obtained. Our study quantifies the consistency between the constraints on $L$ from the neutron-skin data of PREXII and isovector giant dipole resonance (IVGDR) data, and the constraints on $K_0$ from the data of ISGMR in $^{208}$Pb and $^{120}$Sn.
Bayesian approach for many-body uncertainties in nuclear structure: Many-body perturbation theory for finite nuclei
Technische Universität Darmstadt（达姆施塔特工业大学）；ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum für Schwerionenforschung GmbH（极端物质研究所 (EMMI)，GSI 重离子研究中心有限公司）；Max-Planck-Institut für Kernphysik（马克斯·普朗克核物理研究所）
NucleiML: A machine learning framework of ground-state properties of finite nuclei for accelerated Bayesian exploration
Birla Institute of Technology and Science（比拉理工科学与技术学院）；Université Libre de Bruxelles（布鲁塞尔自由大学）；Universidad Andrés Bello（安德烈斯贝略大学）；Saha Institute of Nuclear Physics（萨哈核物理研究所）；Homi Bhabha National Institute（霍米·巴巴国立研究所）
Correlation between nuclear isospin asymmetry and $α$-particle preformation probability for superheavy nuclei from a Bayesian inference
University of South China（南华大学）；School of Mathematics and Physics, University of South China（南华大学数学与物理学院）；Hunan Provincial Key Laboratory of Mathematical Modeling and Scientific Computing, University of South China（南华大学湖南省数学建模与科学计算重点实验室）；School of Nuclear Science and Technology, University of South China（南华大学核科学与技术学院）；School of Physical Science and Technology, Southwest Jiaotong University（西南交通大学物理科学与技术学院）
Constraining Energy Density Functionals via Bayesian Analysis of Nuclear Densities
The University of Osaka（大阪大学）；RIKEN Nishina Center for Accelerator-Based Science（理化学研究所仁科加速器科学研究中心）；Center for Computational Sciences, University of Tsukuba（筑波大学计算科学中心）
Beyond Constant Error: Heteroscedastic Bayesian Model Combination for Modeling Unmeasured Nuclei
Facility for Rare Isotope Beams, Michigan State University（稀有同位素束设施，密歇根州立大学）；Department of Physics and Astronomy, Michigan State University（物理与天文学系，密歇根州立大学）；University of Zagreb（萨格勒布大学）；Institut für Theoretische Physik, Universität Erlangen（理论物理研究所，埃尔朗根大学）
Bayesian neural network with autoencoder for model-based description of $α$-particle preformation factor
School of Mathematics and Physics, University of South China（南华大学数学与计算机学院）；School of Nuclear Science and Technology, University of South China（南华大学核科学技术学院）；School of Physical Science and Technology, Southwest Jiaotong University（西南交通大学物理科学与技术学院）
The East Lansing Model: a Bayesian uncertainty quantified optical potential for rare isotopes
Facility for Rare Isotope Beams, Michigan State University（稀有同位素束设施，密歇根州立大学）；Department of Physics and Astronomy, Michigan State University（物理与天文学系，密歇根州立大学）
Statistical uncertainty quantification for multireference covariant density functional theory
School of Physics and Astronomy, Sun Yat-sen University（中山大学物理学院）；Department of Physics, Kyoto University（京都大学物理系）；Graduate School of China Academy of Engineering Physics（中国工程物理研究院研究生院）；Yukawa Institute for Theoretical Physics, Kyoto University（京都大学汤川理论物理研究所）
Quantifying uncertainty in physics-based predictions of rare-isotope production cross sections via Bayesian-inspired model averaging across nuclear mass tables
Facility for Rare Isotope Beams, Michigan State University（稀有同位素束流设施，密歇根州立大学）
Bayesian inference of nuclear symmetry energy from measured and imagined neutron skin thickness in $^{116,118,120,122,124,130,132}$Sn, $^{208}$Pb, and $^{48}$Ca
重核中的中子皮厚度$Δr_{np}$被认为是探测核物质饱和密度$ρ_0$的最灵敏的地面探测器之一。现有的中子皮数据大多来自强子观测，具有很大的不确定性，并且从实验中提取的数据往往强烈依赖于模型。在急切地等待来自宇称违反电子散射实验的、与模型无关的、高精度的$^{208}$ Pb和$^{48}$ Ca的中子皮数据的同时（JLab的PREX-II和CREX以及MESA的MREX），在使用Skyrme-Hartree-Fock模型的贝叶斯统计框架内，我们推断后验概率分布函数（PDF）从想象的$Δr_{np}(^{208}$ Pb $)=0.15$，0.20和0.30 fm以及$Δr_{np}(^{48}$ Ca $)=0.12$，0.15和0.25 fm，以及不同的$1σ$误差条，计算$ρ_0$处核对称能的斜率参数$L$。的$L$推断使用相同的方法从现有的$Δr_{np}$数据的Sn同位素强子探针的PDF的结果进行了比较。它们还比较了最近的贝叶斯分析的结果的半径和潮汐变形数据的正则中子星从GW 170817和NICER。Sn同位素的中子皮数据分别给出了围绕其平均值的$L=45.5^{+26.5}_{-21.6}$ MeV或围绕其最大值的$L=53.4^{+18.6}_{-29.5}$ MeV { \it a posteriori}，后者小于但与中子星数据的$L=66_{-20}^{+12…
Input-driven analysis in predicting nuclear charge radii using Monte Carlo dropout Bayesian neural network
School of Physics, Ningxia University（宁夏大学物理学院）；Key Laboratory of Beam Technology of Ministry of Education, School of Physics and Astronomy, Beijing Normal University（北京师范大学物理与天文学院教育部光束技术重点实验室）；Guangxi Key Laboratory of Nuclear Physics and Technology, Guangxi Normal University（广西师范大学核物理与技术广西重点实验室）
