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Analytical Gaussian process cosmography: unveiling insights into matter-energy density parameter at present

Bikash R. Dinda

2024The European Physical Journal C12 citationsDOIOpen Access PDF

Abstract

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 $$\Omega _{\textrm{m0}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Ω</mml:mi> <mml:mtext>m0</mml:mtext> </mml:msub> </mml:math> 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 $$\Omega _{\textrm{m0}}h^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>Ω</mml:mi> <mml:mtext>m0</mml:mtext> </mml:msub> <mml:msup> <mml:mi>h</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> , revealing $$\Omega _\textrm{m0}h^2=0.139\pm 0.017$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>Ω</mml:mi> <mml:mtext>m0</mml:mtext> </mml:msub> <mml:msup> <mml:mi>h</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>=</mml:mo> <mml:mn>0.139</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.017</mml:mn> </mml:mrow> </mml:math> . Moreover, leveraging $$H_0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>H</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> observations, we further constrain $$\Omega _{\textrm{m0}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Ω</mml:mi> <mml:mtext>m0</mml:mtext> </mml:msub> </mml:math> , uncovering an inverse correlation between mean $$H_0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>H</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> and $$\Omega _{\textrm{m0}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Ω</mml:mi> <mml:mtext>m0</mml:mtext> </mml:msub> </mml:math> . Our investigation offers a proof of concept for analytical GP cosmography, highlighting the advantages of analytical methods in cosmological parameter estimation.

Topics & Concepts

Gaussian processStatistical physicsProcess (computing)GaussianEnergy (signal processing)Energy densityPhysicsEconometricsTheoretical physicsComputer scienceMathematicsQuantum mechanicsOperating systemCosmology and Gravitation TheoriesAdvanced Thermodynamics and Statistical MechanicsGalaxies: Formation, Evolution, Phenomena