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A Nonparametric Reconstruction of the Hubble Parameter H(z) Based on Radial Basis Function Neural Networks

dc.contributor.authorJian-Chen Zhang
dc.contributor.authorYu Hu
dc.contributor.authorKang Jiao
dc.contributor.authorHong-Feng Wang
dc.contributor.authorYuan-Bo Xie
dc.contributor.authorBo Yu
dc.contributor.authorLi-Li Zhao
dc.contributor.authorTong-Jie Zhang
dc.contributor.otherShandong College of Electronic Technology , Jinan, 250200, People's Republic of China
dc.contributor.otherCollege of Computer and Information Engineering, Dezhou University , Dezhou 253023, People's Republic of China; Institute for Astronomical Science, Dezhou University , Dezhou 253023, People's Republic of China ; tjzhang@bnu.edu.cn
dc.contributor.otherSchool of Physics and Microelectronics, Zhengzhou University , Zhengzhou 450001, People's Republic of China; Department of Astronomy, Beijing Normal University , Beijing 100875, People's Republic of China
dc.contributor.otherCollege of Computer and Information Engineering, Dezhou University , Dezhou 253023, People's Republic of China; Institute for Astronomical Science, Dezhou University , Dezhou 253023, People's Republic of China ; tjzhang@bnu.edu.cn
dc.contributor.otherDepartment of Astronomy, Beijing Normal University , Beijing 100875, People's Republic of China; Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University , Beijing 102206, People's Republic of China
dc.contributor.otherSchool of Mathematics and Big Data, Dezhou University , Dezhou 253023, People's Republic of China
dc.contributor.otherCollege of Computer and Information Engineering, Dezhou University , Dezhou 253023, People's Republic of China; Institute for Astronomical Science, Dezhou University , Dezhou 253023, People's Republic of China ; tjzhang@bnu.edu.cn
dc.contributor.otherInstitute for Astronomical Science, Dezhou University , Dezhou 253023, People's Republic of China ; tjzhang@bnu.edu.cn; Department of Astronomy, Beijing Normal University , Beijing 100875, People's Republic of China; Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University , Beijing 102206, People's Republic of China
dc.date.accessioned2024-01-22T09:54:13Z
dc.date.accessioned2025-10-08T09:13:24Z
dc.date.available2025-10-08T09:13:24Z
dc.date.issued01-01-2024
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/39497
dc.description.abstractAccurately measuring the Hubble parameter is vital for understanding the expansion history and properties of the Universe. In this paper, we propose a new method that supplements the covariance between redshift pairs to improve the reconstruction of the Hubble parameter using the observational Hubble data set. Our approach uses a cosmological model-independent radial basis function neural network to effectively describe the Hubble parameter as a function of redshift. Our experiments show that this method results in a reconstructed Hubble parameter of H _0 = 67.1 ± 9.7 km s ^−1 Mpc ^−1 , which is more noise resistant and fits the ΛCDM model at high redshifts better. Providing the covariance between redshift pairs in subsequent observations will significantly improve the reliability and accuracy of Hubble parametric data reconstruction. Future applications of this method could help overcome the limitations of previous methods and lead to new advances in our understanding of the Universe.
dc.language.isoEN
dc.publisherIOP Publishing
dc.subject.lccAstrophysics
dc.titleA Nonparametric Reconstruction of the Hubble Parameter H(z) Based on Radial Basis Function Neural Networks
dc.typeArticle
dc.description.keywordsObservational cosmology
dc.description.keywordsComputational methods
dc.description.keywordsHubble constant
dc.description.keywordsCosmological parameters
dc.description.doi10.3847/1538-4365/ad0f1e
dc.title.journalThe Astrophysical Journal Supplement Series
dc.identifier.oaioai:doaj.org/journal:516bcd6330334d4eae1207c560cfdc35
dc.journal.infoVolume 270, Issue 2


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