Taxonomy of Dark Energy Models
Abstract
:1. Introduction
2. Basic Equations for the Background Cosmology
3. Cosmological Samples
3.1. Type Ia Supernovae
3.2. Baryon Acoustic Oscillations
3.3. Cosmic Microwave Background Radiation
3.4. Observational Hubble Parameter
3.5. Strong Gravitational Lens Systems
3.6. Ionized Gas in Starburst Galaxies
3.7. Joint Analysis
4. Taxonomy of Dark Energy Models
4.1. Accelerating Universe Fluids
4.1.1. The CDM Model
4.1.2. The CDM Model
4.1.3. Dark Energy Parameterizations
- The Chevallier–Polarski–Linder parametrization (CPL) [99,100]. An approach to study dynamical DE models is through a parametrization of its EoS. The dimensionless Hubble parameter for this Universe is given byWe compute in the same form as is given by Equation (44).The density parameter for DE is written as , and the function depends on as
- The Jassal-Bagla-Padmanabhan (JBP) parametrization. Jassal et al. [102] proposed the following ansatz to parametrize the dark energy EoS,
- The Barbosa–Alcaniz (BA) parametrization. Barboza and Alcaniz [103] considered a EoS given by:This ansatz behaves linearly at low redshifts as , and when . In addition, is well-behaved for all epochs of the Universe. For instance, the DE dynamics in the future, at , can be investigated without dealing with a divergence. Solving the integral in Equation (47) and using Equation (52) results in:
- Feng–Shen–Li–Li (FSLL, [104]) parametrizations.- The authors suggested two dark energy EoS given by:Both functions have the advantage of being divergence-free throughout the entire cosmic evolution, even at . At low redshifts, behaves as and for FSLLI and FSLLII, respectively. In addition, when , the EoS has the same value () as the present epoch for FSLLI and for FSLLII. Using Equations (54) and (55) to solve Equation (47) leads to:
- Sendra–Lazkoz (SL, [105]) introduced new polynomial parameterizations to reduce the parameter correlation, so they can be better constrained by the observations at low redshifts. One of these parameterizations is given by:Notice that, although DE parameterizations are common and they could solve the coincidence problem, there is not a unique way to choose the form of the function. Furthermore, in many cases, there are not strong arguments to justify the functional form by an association with a first-principles theory of quantum fields or gravity. A different approach, which is model-independent, consists of, for example investigating the cosmographic parameters that characterize the kinematics of the cosmic expansion (e.g., [106,107,108,109,110]). Some authors have used the Hubble parameter, the deceleration parameter (), or even higher-order derivatives of the scale factor a, such as Jerk and Snap (e.g., [111,112]). By estimating these cosmographic parameters using cosmological data, it is possible to associate its features to a given DE model (see [111,113,114,115,116,117]).The cosmological constraints for the aforementioned models are obtained assuming flat priors on the DE parameters and a Gaussian prior on h. Table 2 provides the mean values for the , , and () parameters of the JBP, BA, FSLLI, FSLLII, and SL DE parameterizations using the joint of the OHD sample (34 data points from DA and BAO measurements) in the redshift range [118], distance posteriors from Planck [119], and different BAO measurements (see details in [120]). Figure 3 shows the reconstruction of for these parametrizations using the parameter mean values (top panel) and the and confidence contour of the cosmological constraints (bottom panel). Notice that the DE parameterizations are consistent for and .
4.1.4. Chaplygin-Like Fluid
4.1.5. Viscous Model
- . This model, where is the energy density of dust matter and are constants, is probably the simplest one that successfully reproduces the late accelerated stage of the Universe. Some studies that consider a single fluid in the Universe are presented in [132] (see, for example [133], for a case in a causal theory). Additionally, there are other works that include several components such as radiation and DE [65].
- . In spite of the success of the previous model at late epochs of the Universe, it has problems in early epochs because diverges. This motivates the use of alternative viscosity models such as those proposed by [134], in particular polynomial forms of the redshift.
- and . Alternatively, more complex models are investigated in [134] by proposing the viscosity as a hyperbolic function of the dimensionless Hubble parameter E.
4.1.6. Interacting Viscous Models
4.1.7. Phenomenological Emergent Dark Energy Model
4.1.8. Generalized Emergent Dark Energy
4.2. Modifications to General Theory of Relativity
4.2.1. Constant Brane Tension
4.2.2. Variable Brane Tension
4.2.3. Unimodular Gravity
4.2.4. Einstein–Gauss–Bonet
4.2.5. Cardassian Models
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data | ||||
OHD | ||||
SNIa | ||||
CMB | ||||
Joint |
DE Parametrizations | |||||
---|---|---|---|---|---|
Model | |||||
JBP | |||||
BA | |||||
FSLLI | |||||
FSLLII | |||||
SL |
Data Set | OHD | JLA | Joint |
---|---|---|---|
h | |||
a | - | ||
b | - | ||
- | |||
- |
Data | n | h | ||||
---|---|---|---|---|---|---|
OHD | - | |||||
SNIa | ||||||
OHD+SNIa |
Tanh Model | |||||
---|---|---|---|---|---|
Data | b | n | h | ||
OHD | - | ||||
SNIa | |||||
OHD+SNIa | |||||
Cosh Model | |||||
Data | b | n | h | ||
OHD | - | ||||
SNIa | |||||
OHD+SNIa |
Sample | h | |||||
---|---|---|---|---|---|---|
OHD | – | – | ||||
SNIa | – | |||||
SLS | – | – | ||||
Joint | – | |||||
OHD | – | |||||
SNIa | ||||||
SLS | – | |||||
Joint |
Model | h | ||||
---|---|---|---|---|---|
IVM | |||||
IM | 0 | ||||
VM | 0 | ||||
LCDM | 0 | 0 |
Sample | h | ||||
---|---|---|---|---|---|
PEDE | |||||
homogeneous OHD | () | () | () | 0 | |
non-homogeneous OHD | () | () | () | 0 | |
DA OHD | () | () | () | 0 |
Sample | h | ||||
---|---|---|---|---|---|
GEDE | |||||
homogeneous OHD | () | () | () | () | () |
non-homogeneous OHD | () | () | () | () | () |
DA OHD | () | () | () | () | () |
Sample | h | ||||
---|---|---|---|---|---|
OHD | <−0.88 | ||||
BAO | <−9.52 | ||||
SNIa | <−0.31 | ||||
CMB | <−15.0 | ||||
Joint | <−16.2 |
Sample | h | n | eV | ||
---|---|---|---|---|---|
OHD | |||||
BAO | |||||
SNIa | |||||
CMB | |||||
Joint |
Sample | h | ||
---|---|---|---|
OHD | |||
SnIa | |||
CMB | |||
BAO | |||
Joint |
Sample | h | ||||
---|---|---|---|---|---|
OHD | – | ||||
BAO | – | ||||
SNIa | |||||
SLS | – | ||||
HIIG | – | ||||
Joint |
Orignal Cardassian | |||||
---|---|---|---|---|---|
Data | n | l | h | ||
OHD (DA) | |||||
SNIa | |||||
Joint | |||||
Modified polytropic Cardassian | |||||
OHD (DA) | |||||
SNIa | |||||
Joint |
Model | h | Sample | Prior |
---|---|---|---|
wCDM | OHD, SNIa, CMB | Gaussian ( | |
CPL | OHD | Flat [0.2,1.0] | |
JBP | OHD, CMB, BAO | Gaussian () | |
BA | OHD, CMB, BAO | Gaussian () | |
FSLLI | OHD, CMB, BAO | Gaussian () | |
FSLLII | OHD, CMB, BAO | Gaussian () | |
SL | OHD, CMB, BAO | Gaussian () | |
Chaplygin-Like Fluid | OHD, SNIa | Gaussian () | |
Viscous (Polynomial) | OHD, SNIa | Gaussian () | |
Viscous (Hyperbolic tanh) | OHD, SNIa | Gaussian () | |
Viscous (Hyperbolic cosh) | OHD, SNIa | Gaussian () | |
Viscous ( Constant) | OHD, SNIa, SLS | Gaussian () | |
Viscous ( Polynomial) | OHD, SNIa, SLS | Gaussian () | |
Interacting Viscous | OHD | Gaussian () | |
PEDE | Homogeneous OHD | Gaussian () | |
GEDE | Homogeneous OHD | Gaussian () | |
CBT | BAO, SNIa, OHD, CMB | Gaussian () | |
VBT | OHD, SNIa, BAO, BAO | Gaussian () | |
UG | OHD, SNIa, CMB, BAO | Flat [0.2,1.0] | |
EGB | SNIa, BAO, OHD, SLS, HIIG | Gaussian () | |
Original Cardassian | SNIa, OHD | Flat [0,1] | |
Modified Polytropic Cardassian | SNIa, OHD | Flat [0,1] |
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Motta, V.; García-Aspeitia, M.A.; Hernández-Almada, A.; Magaña, J.; Verdugo, T. Taxonomy of Dark Energy Models. Universe 2021, 7, 163. https://doi.org/10.3390/universe7060163
Motta V, García-Aspeitia MA, Hernández-Almada A, Magaña J, Verdugo T. Taxonomy of Dark Energy Models. Universe. 2021; 7(6):163. https://doi.org/10.3390/universe7060163
Chicago/Turabian StyleMotta, Verónica, Miguel A. García-Aspeitia, Alberto Hernández-Almada, Juan Magaña, and Tomás Verdugo. 2021. "Taxonomy of Dark Energy Models" Universe 7, no. 6: 163. https://doi.org/10.3390/universe7060163
APA StyleMotta, V., García-Aspeitia, M. A., Hernández-Almada, A., Magaña, J., & Verdugo, T. (2021). Taxonomy of Dark Energy Models. Universe, 7(6), 163. https://doi.org/10.3390/universe7060163