Checking the second law at cosmic scales

Banerjee, Narayan; Mukherjee, Purba; Pavón, Diego

doi:10.1088/1475-7516/2023/11/092

J. Cosmol. Astropart. Phys.

2023

DOI: 10.1088/1475-7516/2023/11/092

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Checking the second law at cosmic scales

Narayan Banerjee,

Purba Mukherjee,

Diego Pavón

Abstract: Based on recent data about the history of the Hubble factor, it is argued that the second law of thermodynamics holds at the largest scales accessible to observation. This is consistent with previous studies of the same question.

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2024

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Cited by 3 publications

References 44 publications

(47 reference statements)

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A possible late-time transition of M _B inferred via neural networks

Mukherjee,

Dialektopoulos,

Said

et al. 2024

J. Cosmol. Astropart. Phys.

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The strengthening of tensions in the cosmological parameters has led to reconsidering the fundamental aspects of standard cosmology. The tension in the Hubble constant can also be viewed as a tension between local and early Universe constraints on the absolute magnitude M B of Type Ia supernova. In this work, we reconsider the possibility of a variation of this parameter in a model-independent way. We employ neural networks to agnostically constrain the value of the absolute magnitude as well as assess the impact and statistical significance of a variation in M B with redshift from the Pantheon+ compilation, together with a thorough analysis of the neural network architecture. We find an indication for a possible transition redshift at the z ≈ 1 region.

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A possible late-time transition of M _B inferred via neural networks

Mukherjee,

Dialektopoulos,

Said

et al. 2024

J. Cosmol. Astropart. Phys.

View full text Add to dashboard Cite

show abstract

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

Dinda

2024

Eur. Phys. J. C

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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 $$\Omega _{\textrm{m0}}$$ Ω m0 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 $$\Omega _{\textrm{m0}}h^2$$ Ω m0 h 2 , revealing $$\Omega _\textrm{m0}h^2=0.139\pm 0.017$$ Ω m0 h 2 = 0.139 ± 0.017 . Moreover, leveraging $$H_0$$ H 0 observations, we further constrain $$\Omega _{\textrm{m0}}$$ Ω m0 , uncovering an inverse correlation between mean $$H_0$$ H 0 and $$\Omega _{\textrm{m0}}$$ Ω m0 . Our investigation offers a proof of concept for analytical GP cosmography, highlighting the advantages of analytical methods in cosmological parameter estimation.

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A comprehensive data-driven odyssey to explore the equation of state of dark energy

Dinda,

Banerjee

2024

Eur. Phys. J. C

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For the first time, we reconstruct the dark energy equation of the state parameter w from the combination of background and perturbation observations, specifically combining the Hubble parameter data from cosmic chronometer observations and the logarithmic growth rate data from the growth rate observations. We do this analysis using posterior Gaussian process regression without considering any specific cosmological model or parametrization. However there are three main assumptions: (I) a flat Friedmann–Lemaître–Robertson–Walker (FLRW) metric is considered for the cosmological background, (II) there is no interaction between dark energy and matter sectors, and (III) for the growth of inhomogeneity, sub-Hubble approximation and linear perturbations are considered. This study is unique in the sense that the reconstruction of w is independent of any derived parameters such as the present values of the matter-energy density parameter and Hubble parameter. From the reconstruction, we look at how the dark energy equation of state evolves between redshifts 0 and 1.5, finding a slight hint of dynamical behavior in dark energy. However, the evidence is not significant. We also find a leaning towards non-phantom behavior over phantom behavior. We observe that the $$\varLambda $$ Λ CDM model $$(w=-1)$$ ( w = - 1 ) nearly touches the lower boundary of the 1$$\sigma $$ σ confidence region in the redshift range $$0.6 \lesssim z \lesssim 0.85.$$ 0.6 ≲ z ≲ 0.85 . However, it comfortably resides within the 2$$\sigma $$ σ confidence region in the whole redshift range under investigation, $$0\le z \le 1.5.$$ 0 ≤ z ≤ 1.5 . Consequently, the non-parametric, model-independent reconstruction of dark energy provides no compelling evidence to deviate from the $$\varLambda $$ Λ CDM model when considering cosmic chronometer and growth rate observations.

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Checking the second law at cosmic scales

Abstract: Based on recent data about the history of the Hubble factor, it is argued that the second law of thermodynamics holds at the largest scales accessible to observation. This is consistent with previous studies of the same question.

Cited by 3 publications

References 44 publications

A possible late-time transition of M _B inferred via neural networks

A possible late-time transition of M _B inferred via neural networks

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

A comprehensive data-driven odyssey to explore the equation of state of dark energy

Contact Info

Product

Resources

About

Checking the second law at cosmic scales

Abstract: Based on recent data about the history of the Hubble factor, it is argued that the second law of thermodynamics holds at the largest scales accessible to observation. This is consistent with previous studies of the same question.

Cited by 3 publications

References 44 publications

A possible late-time transition of M B inferred via neural networks

A possible late-time transition of M B inferred via neural networks

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

A comprehensive data-driven odyssey to explore the equation of state of dark energy

Contact Info

Product

Resources

About

A possible late-time transition of M _B inferred via neural networks

A possible late-time transition of M _B inferred via neural networks