Reconstruction Of Dark Energy Research Articles

Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory

In this paper, we present an analysis of Supernova Ia (SNIa) distance moduli (μ(z)) and dark energy using an Artificial Neural Network (ANN) reconstruction based on LSST simulated three-year SNIa data. The ANNs employed in this study utilize genetic algorithms for hyperparameter tuning and Monte Carlo Dropout for predictions. Our ANN reconstruction architecture is capable of modeling both the distance moduli and their associated statistical errors given redshift values. We compare the performance of the ANN-based reconstruction with two theoretical dark energy models: ΛCDM and Chevallier–Linder–Polarski (CPL). Bayesian analysis is conducted for these theoretical models using the LSST simulations and compared with observations from Pantheon and Pantheon+ SNIa real data. We demonstrate that our model-independent ANN reconstruction is consistent with both theoretical models. Performance metrics and statistical tests reveal that the ANN produces distance modulus estimates that align well with the LSST dataset and exhibit only minor discrepancies with ΛCDM and CPL.

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

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 Λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varLambda $$\\end{document}CDM model (w=-1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(w=-1)$$\\end{document} nearly touches the lower boundary of the 1σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma $$\\end{document} confidence region in the redshift range 0.6≲z≲0.85.\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.6 \\lesssim z \\lesssim 0.85.$$\\end{document} However, it comfortably resides within the 2σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma $$\\end{document} confidence region in the whole redshift range under investigation, 0≤z≤1.5.\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0\\le z \\le 1.5.$$\\end{document} Consequently, the non-parametric, model-independent reconstruction of dark energy provides no compelling evidence to deviate from the Λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varLambda $$\\end{document}CDM model when considering cosmic chronometer and growth rate observations.

Reconstruction of DBI-essence dark energy with f(R) gravity and its effect on black hole and wormhole mass accretion

In this paper, we have used the reconstructed Dirac–Born–Infeld (DBI)-essence dark energy density to modify the mass accretions of black holes and wormholes. In general, the black hole mass accretion does not depend on the metric or local Einstein geometry. That is why we have used a generalized mechanism by reconstructing the DBI-essence dark energy reconstruction with [Formula: see text] gravity. We have used some particular forms of the scale factor to analyze the accretion phenomena. We have shown the effect of cosmic evolution in the proper time variation of black hole mass accretion. Finally, we have studied the validity of energy conditions and analyzed the Type I–IV singularities for our reconstructed model.

The Reconstruction of Dark Energy with the Ridge Regression Approach

Abstract Whether or not dark energy evolves with time may be determined by the nonparametric method. In order to avoid instability of the derivative for the functional data, we linearize the luminosity-distance integral formula in near-flat space by adopting Lagrange interpolation for the numerical integral, and proposing a method of combining principal component analysis (PCA) and biased estimation on the basis of ridge regression analysis to reconstruct the regression parameters. We also present a principal component selection criterion to better distinguish between ΛCDM and w(z) ≠ −1 models by reconstruction. We define the type I error as the situation where w true = −1 but w recon ≠ −1, and the type II error as the situation where w true ≠ −1 but w recon = −1; we use the various w(z) functions to test the method. The preliminary test results demonstrate that the PCA-biased method can be used to determine the most probable behavior of w(z). Finally, we apply this method to recent supernova measurements, reconstructing the continuous history of w(z) out to redshift z = 1.5.

Non-parametric reconstruction of dark energy and cosmic expansion from the Pantheon compilation of type Ia supernovae * * Supported by the National Natural Science Fund of China (11603005 and 11775038)

The equation of state (EoS) of dark energy plays an important role in the evolution of the universe and has attracted considerable interest in the recent years. With the progress in observational technique, a precise constraint on the EoS of dark energy can be obtained. In this study, we reconstruct the EoS of dark energy and cosmic expansion using Gaussian processes (GP) from the most up-to-date Pantheon compilation of type Ia supernovae (SNe Ia), which consists of 1048 finely calibrated SNe Ia. The reconstructed EoS of dark energy has a large uncertainty owing to its dependence on the second-order derivative of the construction. Adding the direct measurements of Hubble parameters as an additional constraint on the first-order derivative can partially reduce the uncertainty; however, it is still not sufficiently precise to distinguish between the evolving and the constant dark energy. Moreover, the results heavily rely on the prior of the Hubble constant . The value inferred from SNe+ without prior is . Moreover, the matter density has a non-negligible effect on the reconstruction of dark energy. Therefore, more accurate determinations on and are required to tightly constrain the EoS of dark energy.

Dark energy reconstruction based on the Pad\xe9 approximation; an expansion around the varLambda CDM

We study the dynamical properties of dark energy based on a large family of Padé parameterizations for which the dark energy density evolves as the ratio between two polynomials in the scale factor of the universe. Using the latest cosmological data we perform a standard likelihood analysis in order to place constraints on the main cosmological parameters of different Padé models. We find that the basic cosmological parameters, namely ({varOmega _{m0}},h,{sigma _{8}}) are practically the same for all Padé parametrizations explored here. Concerning the free parameters which are related to dark energy we show that the best-fit values indicate that the equation of state parameter at the present time is in the phantom regime (w<-1); however, we cannot exclude the possibility of w>-1 at 1sigma level. Finally, for the current family of Padé parametrizations we test their ability, via AIC, BIC and Jeffreys’ scale, to deviate from varLambda CDM cosmology. Among the current Padé parametrizations, the model which contains two dark energy parameters is the one for which a small but non-zero deviation from varLambda CDM cosmology is slightly allowed by the AIC test. Moreover, based on Jeffreys’ scale we show that a deviation from varLambda CDM cosmology is also allowed and thus the possibility of having a dynamical dark energy in the form of Padé parametrization cannot be excluded.

Gaussian processes reconstruction of dark energy from observational data

In the present paper, we investigate the dark energy equation of state using the Gaussian processes analysis method, without confining a particular parametrization. The reconstruction is carried out by adopting the background data including supernova and Hubble parameter, and perturbation data from the growth rate. It suggests that the background and perturbation data both present a hint of dynamical dark energy. However, the perturbation data have a more promising potential to distinguish non-evolution dark energy including the cosmological constant model. We also test the influence of some parameters on the reconstruction. We find that the matter density parameter Omega _{m0} has a slight effect on the background data reconstruction, but has a notable influence on the perturbation data reconstruction. While the Hubble constant presents a significant influence on the reconstruction from background data.

Curvature dark energy reconstruction through different cosmographic distance definitions

In the context of gravity, dark energy is a geometrical fluid with negative equation of state. Since the function is not known a priori, the need of a model independent reconstruction of its shape represents a relevant technique to determine which model is really favored with respect to others. To this aim, cosmography is related to a generic and its derivatives in order to provide a model‐independent investigation at redshift . The analysis is based on the use of three different cosmological distance definitions, in order to alleviate the duality problem, i.e. the problem of which cosmological distance to use with specific cosmic data sets. We therefore consider the luminosity, , flux, , and angular, , distances and we find numerical constraints by the Union 2.1 supernovae compilation and measurement of baryonic acoustic oscillations, at . We notice that all distances reduce to the same expression, i.e. , at first order. Thus, to fix the cosmographic series of observables, we impose the initial value of H0 by fitting through supernovae only, in the redshift regime . We find that the pressure of curvature dark energy fluid is slightly lower than the one related to the cosmological constant. This indicates that a possible evolving curvature dark energy realistically fills the current universe. Moreover, the combined use of and shows that the sign of the acceleration parameter agrees with theoretical bounds, while its variation, namely the jerk parameter, is compatible with . Finally, we infer the functional form of by means of a truncated polynomial approximation, in terms of fourth order scale factor .

Nonparametric reconstruction of dynamical dark energy via observational Hubble parameter data

We study the power of current and future observational Hubble parameter data (OHD) on non-parametric estimations of the dark energy equation of state, $w(z)$. We propose a new method by conjunction of principal component analysis (PCA) and the criterion of goodness of fit (GoF) criterion to reconstruct $w(z)$, ensuring the sensitivity and reliability of the extraction of features in the EoS. We also give an new error model to simulate future OHD data, to forecast the power of future OHD on the EoS reconstruction. The result shows that current OHD, despite in less quantity, give not only a similar power of reconstruction of dark energy compared to the result given by type Ia supernovae, but also extend the constraint on $w(z)$ up to redshift $z\simeq2$. Additionally, a reasonable forecast of future data in more quantity and better quality greatly enhances the reconstruction of dark energy.

Reconstruction of dark energy and expansion dynamics using Gaussian processes

An important issue in cosmology is reconstructing the effective darkenergy equation of state directly from observations. With fewphysically motivated models, future dark energy studies cannot only bebased on constraining a dark energy parameter space, as the errorsfound depend strongly on the parametrisation considered. We present anew non-parametric approach to reconstructing the history of theexpansion rate and dark energy using Gaussian Processes, which is afully Bayesian approach for smoothing data. We present a pedagogicalintroduction to Gaussian Processes, and discuss how it can be used torobustly differentiate data in a suitable way. Using this method weshow that the Dark Energy Survey - Supernova Survey (DES) canaccurately recover a slowly evolving equation of state to σw = ±0.05 (95% CL) at z = 0 and ±0.25 at z = 0.7, with a minimumerror of ±0.025 at the sweet-spot at z ∼ 0.16, provided theother parameters of the modelare known. Errors on the expansion history are an order of magnitudesmaller, yet make no assumptions about dark energy whatsoever. A codefor calculating functions and their first three derivatives usingGaussian processes has been developed and is available fordownload.

Reconstruction of dark energy and equilibrium thermodynamics in Brans-Dicke theory

Both dark energy and the thermodynamics on apparent horizon in cosmology have been broadly investigated in recent several years. In order to maintain the continuity equation of the total matter in the universe, a new interacting dark energy in the framework of Brans-Dicke theory is proposed. Considering this new interacting dark energy, an equilibrium thermodynamics in Brans-Dicke theory is constructed successfully. Moreover, this new interacting dark energy can be regarded as arising from the “Holographic Dark Energy” models.

Direct Reconstruction of Dark Energy

An important issue in cosmology is reconstructing the effective dark energy equation of state directly from observations. With so few physically motivated models, future dark energy studies cannot only be based on constraining a dark energy parameter space. We present a new nonparametric method which can accurately reconstruct a wide variety of dark energy behavior with no prior assumptions about it. It is simple, quick and relatively accurate, and involves no expensive explorations of parameter space. The technique uses principal component analysis and a combination of information criteria to identify real features in the data, and tailors the fitting functions to pick up trends and smooth over noise. We find that we can constrain a large variety of w(z) models to within 10%-20% at redshifts z≲1 using just SNAP-quality data.

Holographic Ricci dark energy: Current observational constraints, quintom feature, and the reconstruction of scalar-field dark energy

In this work, we consider the cosmological constraints on the holographic Ricci dark energy proposed by Gao et al. [Phys. Rev. D 79, 043511 (2009)], by using the observational data currently available. The main characteristic of holographic Ricci dark energy is governed by a positive numerical parameter $\ensuremath{\alpha}$ in the model. When $\ensuremath{\alpha}l1/2$, the holographic Ricci dark energy will exhibit a quintomlike behavior; i.e., its equation of state will evolve across the cosmological-constant boundary $w=\ensuremath{-}1$. The parameter $\ensuremath{\alpha}$ can be determined only by observations. Thus, in order to characterize the evolving feature of dark energy and to predict the fate of the Universe, it is of extraordinary importance to constrain the parameter $\ensuremath{\alpha}$ by using the observational data. In this paper, we derive constraints on the holographic Ricci dark energy model from the latest observational data including the Union sample of 307 type Ia supernovae, the shift parameter of the cosmic microwave background given by the five-year Wilkinson Microwave Anisotropy Probe observations, and the baryon acoustic oscillation measurement from the Sloan Digital Sky Survey. The joint analysis gives the best-fit results (with $1\ensuremath{\sigma}$ uncertainty): $\ensuremath{\alpha}={0.359}_{\ensuremath{-}0.025}^{+0.024}$ and ${\ensuremath{\Omega}}_{\mathrm{m}0}={0.318}_{\ensuremath{-}0.024}^{+0.026}$. That is to say, according to the observations, the holographic Ricci dark energy takes on the quintom feature. Finally, in light of the results of the cosmological constraints, we discuss the issue of the scalar-field dark energy reconstruction, based on the scenario of the holographic Ricci vacuum energy.

Impact of the matter density uncertainty on the dark energy reconstruction

In this paper we study the impact of the fractional matter density uncertainty in the reconstruction of the equation of state of dark energy. We consider both standard reconstruction methods, based on the dynamical effect that dark energy has on the expansion of the Universe, as well as nonstandard methods, in which the evolution of the dark energy equation of state with redshift is inferred through the variation of fundamental couplings such as the fine-structure constant, $\ensuremath{\alpha}$, or the proton-to-electron mass ratio, $\ensuremath{\mu}$. We show that the negative impact of the matter density uncertainty in the dark energy reconstruction using varying couplings may be very small compared to standard reconstruction methods. We also briefly discuss other fundamental questions which need to be answered before varying couplings can be successfully used to probe the nature of the dark energy.

Dark energy FRW cosmology – dynamical system reconstruction

We develop a simple method of dark energy reconstruction using a geometrical form of the luminosity-distance relation. In this method the FRW dynamical system with dark energy is reconstructed instead of the equation of state parameter. We give several examples which illustrate the usefulness of our method in fitting the redshift transition from the decelerating to accelerating phase as the value of the Hubble function at the transition.

RECONSTRUCTING DARK ENERGY

This review summarizes recent attempts to reconstruct the expansion history of the universe and to probe the nature of dark energy. Reconstruction methods can be broadly classified into parametric and non-parametric approaches. It is encouraging that, even with the limited observational data currently available, different approaches give consistent results for the reconstruction of the Hubble parameter H(z) and the effective equation of state w(z) of dark energy. Model independent reconstruction using current data allows for modest evolution of dark energy density with redshift. However, a cosmological constant (= dark energy with a constant energy density) remains an excellent fit to the data. Some pitfalls to be guarded against during cosmological reconstruction are summarized and future directions for the model independent reconstruction of dark energy are explored.