论文标题
使用大都市接受标准对宇宙学参数的进化优化
Evolutionary optimization of cosmological parameters using metropolis acceptance criterion
论文作者
论文摘要
我们引入了一种新型的进化方法，该方法从MCMC方法中获取杠杆作用，该方法可用于约束宇宙学的参数和理论模型。与MCMC技术基本上是设计非平行算法的MCMC技术不同，新提出的算法能够获得多核机的全部潜力。使用该算法，我们可以获得Lambda CDM宇宙学模型的最佳合适参数，并确定哈勃参数$ H_0 $中的差异。在目前的工作中，我们在这里报告了这种新颖方法的设计原理，以及Pantheon，OHD和Planck数据集的分析结果。与其他类似练习相比，参数的估计显示与先前报道的值以及更高的计算性能一致。
We introduce a novel evolutionary method that takes leverage from the MCMC method that can be used for constraining the parameters and theoretical models of Cosmology. Unlike the MCMC technique, which is essentially a non-parallel algorithm by design, the newly proposed algorithm is able to obtain the full potential of multi-core machines. With this algorithm, we could obtain the best-fit parameters of the Lambda CDM cosmological model 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 compared to the other similar exercises.
