利用Metropolis接受准则进行宇宙学参数的进化优化
Evolutionary optimization of cosmological parameters using metropolis acceptance criterion
AI总结：
本文提出一种天然并行的进化算法，利用Metropolis接受准则约束宇宙学参数，能高效获得ΛCDM模型最佳拟合参数及不确定性，并在Pantheon、OHD和Planck数据集上验证了结果的一致性与计算性能优势。
摘要：
本文介绍了一种新颖的进化方法，可用于约束宇宙学的参数和理论模型。新提出的算法在设计上天然具有并行性，能够充分发挥多核机器的全部潜力。利用该算法，我们能够获得$ΛCDM$宇宙学模型的最佳拟合参数及其不确定性，并识别出哈勃参数$H_0$中的差异。在本工作中，我们讨论了这种新方法的设计原理，并报告了对Pantheon、OHD和Planck数据集的分析结果。参数估计与先前报道的值显示出显著的一致性，并且在迭代次数方面，与其他类似工作相比，计算性能更高。
英文摘要：
A novel evolutionary method is introduced that can be used for constraining the parameters and theoretical models of Cosmology. The newly proposed algorithm, which is inherently parallel by design, is able to obtain the full potential of multi-core machines. With this algorithm, we could obtain the best-fit parameters of the $ΛCDM$ cosmological model as well as the uncertainties and identify the discrepancy in the Hubble parameter $H_0$. In the present work we discuss the design principle of this novel approach and also the results from the analysis of Pantheon, OHD and Planck datasets are reported here. The estimation of parameters shows significant consistency with the previously reported values as well as a higher computational performance measured in number iterations compared to the other similar exercises.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
