Analytical gaussian process cosmography: unveiling insights into matter-energy density parameter at present
dc.contributor.author | Dinda, Bikash R | |
dc.date.accessioned | 2025-01-16T07:30:21Z | |
dc.date.available | 2025-01-16T07:30:21Z | |
dc.date.issued | 2024 | |
dc.description.abstract | In this study, we introduce a novel analytical Gaussian Process (GP) cosmography methodology, leveraging the differentiable properties of GPs to derive key cosmological quantities analytically. Our approach combines cosmic chronometer (CC) Hubble parameter data with growth rate (f) observations to constrain the Ωm0 parameter, offering insights into the underlying dynamics of the Universe. By formulating a consistency relation independent of specific cosmological models, we analyze under a flat FLRW metric and first-order Newtonian perturbation theory framework. Our analytical approach simplifies the process of Gaussian Process regression (GPR), providing a more efficient means of handling large datasets while offering deeper interpretability of results. We demonstrate the effectiveness of our methodology by deriving precise constraints on Ωm0h2, revealing Ωm0h2=0.139±0.017. Moreover, leveraging H0 observations, we further constrain Ωm0, uncovering an inverse correlation between mean H0 and Ωm0. Our investigation offers a proof of concept for analytical GP cosmography, highlighting the advantages of analytical methods in cosmological parameter estimation. © The Author(s) 2024. | |
dc.identifier.citation | Dinda, B.R., 2024. Analytical Gaussian process cosmography: unveiling insights into matter-energy density parameter at present. The European Physical Journal C, 84(4), pp.1-12. | |
dc.identifier.uri | https://doi.org/10.1140/epjc/s10052-024-12774-x | |
dc.identifier.uri | https://hdl.handle.net/10566/19791 | |
dc.language.iso | en | |
dc.publisher | Springer Nature | |
dc.subject | Chronometers | |
dc.subject | NATURAL SCIENCES::Physics::Astronomy and astrophysics::Cosmology | |
dc.subject | Gaussian distribution | |
dc.subject | Gaussian noise (electronic) | |
dc.subject | Inverse problems | |
dc.title | Analytical gaussian process cosmography: unveiling insights into matter-energy density parameter at present | |
dc.type | Article |
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