scholarly journals Figure of merit for dark energy constraints from current observational data

2008 ◽  
Vol 77 (12) ◽  
Author(s):  
Yun Wang
Keyword(s):  
Dark Energy ◽  
Observational Data ◽  
Figure Of Merit ◽  
Energy Constraints

scholarly journals FORECASTS OF DARK ENERGY CONSTRAINTS FROM BARYON ACOUSTIC OSCILLATIONS

2010 ◽  
Vol 25 (37) ◽  
pp. 3093-3113 ◽  
Author(s):  
YUN WANG
Keyword(s):  
Dark Energy ◽  
Figure Of Merit ◽  
Real Space ◽  
Number Density ◽  
Acoustic Oscillations ◽  
Energy Constraints ◽  
Redshift Survey ◽  
Galaxy Redshift ◽  
Baryon Acoustic Oscillations ◽  
Systematic Noise

The measurement of baryon acoustic oscillations (BAO) from a galaxy redshift survey provides one of the most promising methods for probing dark energy. In this paper, we clarify the assumptions that go into the forecasts of dark energy constraints from BAO. We show that assuming a constant nP0.2/G2(z) (where P0.2 is the real space galaxy power spectrum at k = 0.2 h/ Mpc and redshift z) gives a good approximation of the observed galaxy number density expected from a realistic flux-limited galaxy redshift survey. We find that assuming nP0.2/G2(z) = 10 gives very similar dark energy constraints to assuming nP0.2 = 3, but the latter corresponds to a galaxy number density larger than ~70% at z = 2. We show how the Figure-of-Merit (FoM) for constraining dark energy depends on the assumed galaxy number density, redshift accuracy, redshift range, survey area, and the systematic errors due to calibration and uncertainties in the theory of nonlinear evolution and galaxy biasing. We find that an additive systematic noise of up to 0.4–0.5% per Δz = 0.1 redshift slice does not lead to significant decrease in the BAO FoM.

scholarly journals Holographic dark energy: constraints on the interaction from diverse observational data sets

2019 ◽  
Vol 134 (4) ◽  
Author(s):  
Purba Mukherjee ◽  
Ankan Mukherjee ◽  
H. K. Jassal ◽  
Ananda Dasgupta ◽  
Narayan Banerjee
Keyword(s):  
Dark Energy ◽  
Observational Data ◽  
Holographic Dark Energy ◽  
Data Sets ◽  
Energy Constraints

scholarly journals Consistency of nonlinear interacting ghost dark energy with recent observations

2017 ◽  
Vol 26 (11) ◽  
pp. 1750124 ◽  
Author(s):  
E. Ebrahimi ◽  
H. Golchin ◽  
A. Mehrabi ◽  
S. M. S. Movahed
Keyword(s):  
Dark Energy ◽  
Observational Data ◽  
Nonlinear Interaction ◽  
Acoustic Oscillation ◽  
Joint Analysis ◽  
Data Sets ◽  
Dark Energy Density ◽  
Ghost Dark Energy ◽  
Interaction Terms ◽  
Best Fit

In this paper, we investigate ghost dark energy model in the presence of nonlinear interaction between dark energy and dark matter. We also extend the analysis to the so-called generalized ghost dark energy (GGDE) which [Formula: see text]. The model contains three free parameters as [Formula: see text] and [Formula: see text] (the coupling coefficient of interactions). We propose three kinds of nonlinear interaction terms and discuss the behavior of equation of state, deceleration and dark energy density parameters of the model. We also find the squared sound speed and search for signs of stability of the model. To compare the interacting GGDE model with observational data sets, we use more recent observational outcomes, namely SNIa from JLA catalog, Hubble parameter, baryonic acoustic oscillation and the most relevant CMB parameters including, the position of acoustic peaks, shift parameters and redshift to recombination. For GGDE with the first nonlinear interaction, the joint analysis indicates that [Formula: see text], [Formula: see text] and [Formula: see text] at 1 optimal variance error. For the second interaction, the best fit values at [Formula: see text] confidence are [Formula: see text], [Formula: see text] and [Formula: see text]. According to combination of all observational data sets considered in this paper, the best fit values for third nonlinearly interacting model are [Formula: see text], [Formula: see text] and [Formula: see text] at [Formula: see text] confidence interval. Finally, we found that the presence of interaction is compatible in mentioned models via current observational datasets.

scholarly journals Comparing dark energy models with current observational data

2018 ◽  
Vol 2018 (07) ◽  
pp. 011-011 ◽  
Author(s):  
Sixiang Wen ◽  
Shuang Wang ◽  
Xiaolin Luo
Keyword(s):  
Dark Energy ◽  
Observational Data ◽  
Energy Models

scholarly journals The effect of inhomogeneities on dark energy constraints

2018 ◽  
Vol 2018 (07) ◽  
pp. 024-024 ◽  
Author(s):  
Suhail Dhawan ◽  
Ariel Goobar ◽  
Edvard Mörtsell
Keyword(s):  
Dark Energy ◽  
Energy Constraints

scholarly journals Exploring the dark universe: Constraints on dynamical dark energy models from CMB, BAO and growth rate measurements

2019 ◽  
Vol 28 (09) ◽  
pp. 1950118 ◽  
Author(s):  
Alexander Bonilla Rivera ◽  
Jorge Enrique García-Farieta
Keyword(s):  
Dark Energy ◽  
Observational Data ◽  
Cosmological Parameters ◽  
Growth Parameter ◽  
Information Criteria ◽  
Fine Tuning ◽  
Coincidence Problem ◽  
Shift Parameter ◽  
History Of ◽  
Dynamical Dark Energy

In order to explain the current acceleration of the universe, the fine-tuning problem of the cosmological constant [Formula: see text] and the cosmic coincidence problem, different alternative models have been proposed in the literature. We use the most recent observational data from CMB (Planck 2018 final data release) and LSS (SDSS, WiggleZ, VIPERS) to constrain dynamical dark energy (DE) models. The CMB shift parameter, which traditionally has been used to determine the main cosmological parameters of the standard model [Formula: see text], is employed in addition to data from redshift-space distortions through the growth parameter [Formula: see text] to constrain the mass variance [Formula: see text]. BAO data are also used to study the history of the cosmological expansion and the main properties of DE. From the evolution of [Formula: see text], we found a slowdown of acceleration behavior at low redshifts, and by using the Akaike and Bayesian Information Criteria (AIC, BIC), we discriminate different models to find those that are better suited to the observational data, finding that the interacting dark energy (IDE) model is the most favored by observational data, including information from SNIa and Hz. The analysis shows that the IDE model is followed closely by EDE and [Formula: see text] models, which in some cases fit better the observational data with individual probes.

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