SN Ia Data Sets Research Articles

FLRW cosmology in Weyl type f(Q) gravity and observational constraints

Inspired by various works in a Weyl type f(Q) gravity to reduce some serious problems in General Relativity Theory (GR), we develop a model of the universe in a Weyl type f(Q) gravity which shows the transition from a decelerating state to an accelerating state of the universe at present when we consider a particular functional form of the f(Q) gravity as f(Q)=(H02)(α1+α2log(H0−2Q)). We numerically solve Weyl type f(Q) gravity field equations and obtain the numerical solutions to the Hubble parameter, deceleration parameter, distance modulus, and the apparent magnitudes of stellar objects using Ia Supernovae. Also, we obtain numerical solutions for the Weyl vector w, non-metricity scalar Q, and the Lagrangian multiplier λ appearing in the action of f(Q) gravity. We compare our model with the error bar plots of the observed Hubble dataset of 77 points, SNIa datasets of 580 points, and 1048 supernova Pantheon datasets of the apparent magnitudes, and the statistical analysis using Baryon Acoustic Oscillations (BAO). It is found that our results fit well with the observed values. The model envisages a unique feature: although the universe is filled with perfect fluid as dust whose pressure is zero, the Weyl vector dominance f(Q) creates acceleration.

Kaniadakis agegraphic dark energy

In this manuscript, we present a novel dark energy scenario based on Kaniadakis entropy, which is a generalization of Boltzmann–Gibbs entropy that arises from relativistic statistical theory and is characterized by a parameter K which quantifies the deviations from standard expressions, and we use the age of the Universe as infrared cutoff. The cosmological characteristics of the proposed dark energy model, as well as the evolution of the cosmos filled with pressure-free matter and the ensuing dark energy candidates, are investigated. The interaction as well as non-interaction among the two sectors will also be considered. The differential equation for the dark energy density parameter, including the expression of the equation of state and deceleration parameters, are derived. The analysis of deceleration parameter clearly shows the universe to transit from decelerated to accelerated phase around z≈0.6. The most recent 30 H(z) data samples and the 580 Union 2.1 SNIa data set were also used to limit the theoretical results. The squared sound speed is also plotted against redshift z to check the stability behavior of the model for both the cases.

Constraining the dark energy models using baryon acoustic oscillations: An approach independent of H0 ⋅ rd

The H0 tension and the accompanying rd tension are a hot topic in current cosmology. In order to remove the degeneracy between the Hubble parameter H0 and the sound horizon scale rd from the baryon acoustic oscillation (BAO) datasets, we redefined the likelihood by marginalizing over the H0 ⋅ rd parameter and then we performed a full Bayesian analysis for different models of dark energy (DE). We find that our datasets that are uncalibrated by early or late physics cannot constrain the DE models properly without further assumptions. By adding the type Ia supernova (SNIa) dataset, the models are constrained better with smaller errors on the DE parameters. The two BAO datasets we used – one with angular measurements and one with angular and radial ones, with their covariance – show statistical preferences for different models, with the Λ cold dark matter (ΛCDM) model being the best model for one of them. Adding the Pantheon SNIa dataset with its covariance matrix boosts the statistical preference for the ΛCDM model.

A hierarchical Bayesian SED model for Type Ia supernovae in the optical to near-infrared

ABSTRACT While conventional Type Ia supernova (SN Ia) cosmology analyses rely primarily on rest-frame optical light curves to determine distances, SNe Ia are excellent standard candles in near-infrared (NIR) light, which is significantly less sensitive to dust extinction. An SN Ia spectral energy distribution (SED) model capable of fitting rest-frame NIR observations is necessary to fully leverage current and future SN Ia data sets from ground- and space-based telescopes including HST, LSST, JWST, and RST. We construct a hierarchical Bayesian model for SN Ia SEDs, continuous over time and wavelength, from the optical to NIR (B through H, or $0.35{-}1.8\, \mu$m). We model the SED as a combination of physically distinct host galaxy dust and intrinsic spectral components. The distribution of intrinsic SEDs over time and wavelength is modelled with probabilistic functional principal components and the covariance of residual functions. We train the model on a nearby sample of 79 SNe Ia with joint optical and NIR light curves by sampling the global posterior distribution over dust and intrinsic latent variables, SED components and population hyperparameters. Photometric distances of SNe Ia with NIR data near maximum obtain a total RMS error of 0.10 mag with our BayeSN model, compared to 0.13–0.14 mag with SALT2 and SNooPy for the same sample. Jointly fitting the optical and NIR data of the full sample up to moderate reddening (host E(B − V) < 0.4) for a global host dust law, we find RV = 2.9 ± 0.2, consistent with the Milky Way average.

Relieve the H0 tension with a new coupled generalized three-form dark energy model

In this work we propose a new coupled generalized three-form dark energy model, in which dark energy are represented by a three-form field and dark matter are represented by point particles that can be regarded as dust. We first perform a dynamical analysis on the new model and obtain four fixed points, including a saddle point representing a radiation dominated Universe, a saddle point representing a matter dominated Universe, and two attractors representing two dark energy dominated Universes. We then use the observational data, including cosmic microwave background (CMB) data, baryon acoustic oscillations (BAO) data, and Type Ia supernovae (SN Ia) data to constrain the model parameters of the coupled generalized three-form dark energy model. For comparison, we also consider the coupled three-form dark energy model, generalized three-form dark energy model, and Λ CDM model, we find that parameters α and β in the coupled generalized three-form dark energy model are essential for such model to reduce the H 0 tension to a more acceptable level, with H 0 = 70 . 1 − 1 . 5 + 1 . 4 km/s/Mpc, which is consistent with R19 at 2.0 σ confidence level. However, analysis with Akaike information criterion (AIC) show that the coupled generalized three-form dark energy model is least supported by CMB, BAO, and SN Ia datasets since the model have more free parameters, analysis also show that the Λ CDM model is most supported by CMB, BAO, and SN Ia datasets although it leads to a strongest H 0 tension. We also investigate the best-fit dynamical behavior of the coupled generalized three-form dark energy model, and show that our model is equivalent to a quintom dark energy model, in which dark energy, at early epoch, behaves like some form of early dark energy with a small positive equation of state.

Exploring the latest Pantheon SN Ia dataset by using three kinds of statistics techniques

In this work, we explore the cosmological consequences of the latest Type Ia supernova (SN Ia) dataset, Pantheon, by adopting the wCDM model. The Pantheon dataset currently contains the largest number of SN Ia samples, which contains 1048 supernovae on the redshift range 0 < z < 2.3. Here we take into account three kinds of SN Ia statistics techniques, including: (1) magnitude statistics (MS), which is the traditional SN Ia statistics technique; (2) flux statistics (FS), which is based on the flux-averaging (FA) method; and (3) improved flux statistics (IFS), which combines the advantages of MS and FS. It should be mentioned that the IFS technique needs to scan the (z cut, Δz) parameters plane, where z cut and Δz are redshift cut-off and redshift interval of FA, respectively. The results are as follows. (1) Using the SN dataset only, the best FA recipe for IFS is (z cut, Δz) = (0.1, 0.08); (2) comparing to the old SN dataset, JLA, adopting the Pantheon dataset can reduce the 2σ error bars of equation of state w by 38%, 47% and 53% for MS, FS and IFS, respectively; (3) FS gives closer results to other observations, such as Baryon acoustic oscillations and cosmic microwave background; (4) compared with FS and IFS, MS more favors a Universe that will end in a ‘big rip’.

Specific Effect of Peculiar Velocities on Dark-energy Constraints from Type Ia Supernovae

Abstract Peculiar velocities of Type Ia supernova (SN Ia) host galaxies affect the dark-energy parameter constraints in a small but very specific way: the parameters are biased in a single direction in parameter space that is a priori knowable for a given SN Ia data set. We demonstrate the latter fact with a combination of inference from a cosmological N-body simulation with overwhelming statistics applied to the Pantheon SN Ia data set, then confirm it by simple quantitative arguments. We quantify small modifications to the current analyses that would ensure that the effect of cosmological parameters is essentially guaranteed to be negligible.

The logotropic dark fluid: Observational and thermodynamic constraints

We have considered a spatially flat, homogeneous and isotropic FLRW universe filled with a single fluid, known as logotropic dark fluid (LDF), whose pressure evolves through a logarithmic equation-of-state. We use the recent Pantheon SNIa and cosmic chronometer datasets to constrain the parameters of this model, the present fraction of DM [Formula: see text] and the Hubble constant [Formula: see text]. We find that the mean values of these parameters are [Formula: see text] and [Formula: see text] at the [Formula: see text] CL. We also find that the LDF model shows a smooth transition from the deceleration phase to acceleration phase of the universe in the recent past. We note that the redshift of this transition [Formula: see text] ([Formula: see text] error) and is well consistent with the present observations. Interestingly, we find that the universe will settle down to a [Formula: see text]CDM model in future and there will not be any future singularity in the LDF model. Furthermore, we notice that there is no significant difference between the LDF and [Formula: see text]CDM models at the present epoch, but the difference (at the percent level) between these models is found as the redshift increases. We have also studied the generalized second law of thermodynamics at the dynamical apparent horizon for the LDF model with the Bekenstein and Viaggiu entropies.

Existence of bulk viscous universe in f(R, T) gravity and confrontation with observational data

In this paper we have investigated a bulk viscous universe in f(R, T) gravity where R and T are the Ricci scalar and trace of energy momentum tensor respectively. We have obtained explicit solutions of field equations in modified gravity by considering the power law form of scale factor. The Hubble parameter and deceleration parameter are derived in terms of cosmic time and redshift both. We have estimated the present values of these parameters with observational Hubble data and SN Ia data sets. At 1σ level, the estimated values of q0 and m are obtained as q0=−0.30±0.05 & m=0.70±0.02 where q0 is the present value of deceleration parameter and m is the model parameter. The energy conditions and Om(z) analysis for the anisotropic LRS Bianchi type I model are also discussed.

Exploring the evidence for a large local void with supernovae Ia data

Abstract In the present work we utilise the most recent publicly available SN Ia compilations and implement a well formulated cosmological model based on LTB metric in presence of cosmological constant Λ (ΛLTB) to test for signatures of large local inhomogeneities at z ≤ 0.15. Local underdensities in this redshift range have been previously found based on luminosity density data and galaxy number counts. Our main constraints on the possible local void using the Pantheon SN Ia dataset are: redshift size of $z_{\rm size}=0.068^{+0.021}_{-0.030}$; density contrast of $\delta \Omega _0/\Omega _0 = -10.5_{-7.4}^{+9.3}\%$ between 16th and 84th percentiles. Investigating the possibility to alleviate the ∼ $9\%$ disagreement between measurements of present expansion rate H0 coming from calibrated local SN Ia and high-z CMB data, we find large local void to be a very unlikely explanation alone, consistently with previous studies. However, the level of matter inhomogeneity at a scale of ∼100Mpc that is allowed by SN Ia data, although not expected from ΛCDM cosmic variance calculations, could be the origin of additonal systematic error in distance ladder measurements based on SN Ia. Fitting low-redshift Pantheon data with a cut 0.023 &lt; z &lt; 0.15 to the ΛLTB model and to the Taylor expanded luminosity distance formula we estimate that this systematic error amounts to $1.1\%$ towards the lower H0 value. A test for local anisotropy in Pantheon SN Ia data yields null evidence. Analysis of luminosity density data provides a constraint on contrast of large isotropic void $\delta \Omega _0/\Omega _0 = -51.9\%\pm 6.3\%$, which is in ∼ 4σ tension with SN Ia results. More data is necessary to better constrain the local matter density profile and understand the disagreement between SN Ia and luminosity density samples.

Steve: A Hierarchical Bayesian Model for Supernova Cosmology

Abstract We present a new Bayesian hierarchical model (BHM) named Steve for performing Type Ia supernova (SN Ia) cosmology fits. This advances previous works by including an improved treatment of Malmquist bias, accounting for additional sources of systematic uncertainty, and increasing numerical efficiency. Given light-curve fit parameters, redshifts, and host-galaxy masses, we fit Steve simultaneously for parameters describing cosmology, SN Ia populations, and systematic uncertainties. Selection effects are characterized using Monte Carlo simulations. We demonstrate its implementation by fitting realizations of SN Ia data sets where the SN Ia model closely follows that used in Steve. Next, we validate on more realistic SNANA simulations of SN Ia samples from the Dark Energy Survey and low-redshift surveys (DES Collaboration et al. 2018). These simulated data sets contain more than 60,000 SNe Ia, which we use to evaluate biases in the recovery of cosmological parameters, specifically the equation of state of dark energy, w. This is the most rigorous test of a BHM method applied to SN Ia cosmology fitting and reveals small w biases that depend on the simulated SN Ia properties, in particular the intrinsic SN Ia scatter model. This w bias is less than 0.03 on average, less than half the statistical uncertainty on w. These simulation test results are a concern for BHM cosmology fitting applications on large upcoming surveys; therefore, future development will focus on minimizing the sensitivity of Steve to the SN Ia intrinsic scatter model.

Anisotropy Effects and Observational Data on the Constraints of Evolution Dark Energy Models

We investigate cosmological dark energy models where the accelerated expansion of the universe is driven by a field with an anisotropic universe. The constraints on the parameters are obtained by maximum likelihood analysis using observational of 194 Type Ia supernovae (SNIa) and the most recent joint light-curve analysis (JLA) sample. In particular we reconstruct the dark energy equation of state parameter w(z) and the deceleration parameter q(z). We find that the best fit dynamical w(z) obtained from the 194 SNIa dataset does not cross the phantom divide line w(z)=−1 and remains above and close to line for the whole redshift range showing no evidence for phantom behavior. By applying the anisotropy effect on the ΛCDM model, the joint analysis indicates that , with 194 SNIa, with 238 the SiFTO sample of JLA and with 1048 the SALT2 sample of Pantheon at confidence interval. The analysis shows that by considering the anisotropy, it leads to more best fit parameters in all models with JLA SNe datasets. Furthermore, we use two statistical tests such as the usual and p-test to compare two dark energy models with ΛCDM model. Finally we show that the presence of anisotropy is confirmed in mentioned models via SNIa dataset.

Effect of low anisotropy and supernova constraints on cosmological models

The 194 supernova Ia data and the effect of anisotropy are combined to reconstruct the dark energy equation of state parameter [Formula: see text] and the deceleration parameter [Formula: see text]. Using the supernovae type Ia data, we evaluate the anisotropy effects (although low) on dark energy parametrization [Formula: see text] and we compare the results with [Formula: see text]CDM model. Present supernova observations are analyzed using a standard [Formula: see text] method and the minimal [Formula: see text] values obtained for each model are compared. We confirm the difficulty of discriminating between these models using present SNIa data only. By means of the maximum likelihood method, we find that the best-fit dynamical [Formula: see text] and [Formula: see text] parameters [Formula: see text] are obtained from the SNIa dataset. In particular, we find the best-fit values of [Formula: see text]CDM model ([Formula: see text] = 0.013, [Formula: see text] = 197.559) for [Formula: see text] = 0.3 and ([Formula: see text] = 0.02, [Formula: see text] = 196.983) for [Formula: see text] = 0.27. Finally, we found that the presence of anisotropy is confirmed in mentioned models via SNIa dataset.

Testing the cosmic anisotropy with supernovae data: Hemisphere comparison and dipole fitting

The cosmological principle is one of the cornerstones in modern cosmology. It assumes that the universe is homogeneous and isotropic on cosmic scales. Both the homogeneity and the isotropy of the universe should be tested carefully. In the present work, we are interested in probing the possible preferred direction in the distribution of type Ia supernovae (SNIa). To our best knowledge, two main methods have been used in almost all of the relevant works in the literature, namely the hemisphere comparison (HC) method and the dipole fitting (DF) method. However, the results from these two methods are not always approximately coincident with each other. In this work, we test the cosmic anisotropy by using these two methods with the Joint Light-Curve Analysis (JLA) and simulated SNIa datasets. In many cases, both methods work well, and their results are consistent with each other. However, in the cases with two (or even more) preferred directions, the DF method fails while the HC method still works well. This might shed new light on our understanding of these two methods.

Falsifying cosmological models based on a non-linear electrodynamics

Recently, the nonlinear electrodynamics (NED) has been gaining attention to generate primordial magnetic fields in the Universe and also to resolve singularity problems. Moreover, recent works have shown the crucial role of the NED on the inflation. This paper provides a new approach based on a new model of NED as a source of gravitation to remove the cosmic singularity at the big bang and explain the cosmic acceleration during the inflation era on the background of stochastic magnetic field. Also, we found a realization of a cyclic Universe, free of initial singularity, due to the proposed NED energy density. In addition, we explore whether a NED field without or with matter can be the origin of the late-time acceleration. For this we obtain explicit equations for H(z) and perform a MCMC analysis to constrain the NED parameters by using 31 observational Hubble data (OHD) obtained from cosmic chronometers covering the redshift range 0< z < 1.97; and with the joint-light-analysis (JLA) SNIa compilation consisting in 740 data points in the range 0.01<z<1.2. All our constraints on the current magnetic field give B_{0}sim 10^{-31}mathrm {cm^{-1}}, which are larger than the upper limit 10^{-33}mathrm {cm^{-1}} by the Planck satellite implying that NED cosmologies could not be suitable to explain the Universe late-time dynamics. However, the current data is able to falsify the scenario at late times. Indeed, one is able to reconstruct the deceleration parameter q(z) using the best fit values of the parameters obtained from OHD and SNIa data sets. If the matter component is not included, the data sets predict an accelerated phase in the early Universe, but a non accelerated Universe is preferred in the current epoch. When a matter component is included in the NED cosmology, the data sets predict a q(z) dynamics similar to that of the standard model. Moreover, both cosmological data favor up to 2sigma confidence levels an accelerating expansion in the current epoch, i.e., the Universe passes of a decelerated phase to an accelerated stage at redshift sim 0.6. Therefore, although the NED cosmology with dust matter predict a value B_{0} higher than the one measured by Planck satellite, it is able to drive a late-time cosmic acceleration which is consistent with our dynamical systems analysis and it is preferred by OHD and SNIa data sets.

Scalar-tensor gravity with scalar-matter direct coupling and its cosmological probe

SNIA and CMB datasets have shown both of evolving Newton's "constant" and a signature of the coupling of scalar field to matter. These observations motivate the consideration of the scalar-matter coupling in Jordan frame in the framework of scalar-tensor gravity. So far, majority of the works on the coupling of scalar matter has performed in Einstein frame in the framework of minimally coupled scalar fields. In this work, we generalize the original scalar-tensor theories of gravity introducing a direct coupling of scalar to matter in the Jordan frame. The combined consideration of both evolving Newton's constant and scalar-matter coupling using the recent observation datasets, shows features different from the previous works. The analysis shows a vivid signature of the scalar-matter coupling. The variation rate of the Newton's constant is obtained rather greater than that determined in the previous works.

The need for accurate redshifts in supernova cosmology

Recent papers have shown that a small systematic redshift shift (Δ z∼ 10−5) in measurements of type Ia supernovae can cause a significant bias (∼1%) in the recovery of cosmological parameters. Such a redshift shift could be caused, for example, by a gravitational redshift due to the density of our local environment. The sensitivity of supernova data to redshift shifts means supernovae make excellent probes of inhomogeneities. We therefore invert the analysis, and try to diagnose the nature of our local gravitational environment by fitting for Δ z as an extra free parameter alongside the usual cosmological parameters. Using the Joint Light-curve SN Ia dataset we find the best fit includes a systematic redshift shift of Δ z = (2.6+2.7−2.8) × 10−4. This is a larger shift than would be expected due to gravitational redshifts in a standard Λ-Cold Dark Matter universe (though still consistent with zero), and would correspond to a monopole Doppler shift of about 100 km s−1 moving away from the Milky-Way. However, since most supernova measurements are made to a redshift precision of no better than 10−3, it is possible that a systematic error smaller than the statistical error remains in the data and is responsible for the shift; or that it is an insignificant statistical fluctuation. We find that when Δ z is included as a free parameter while fitting to the JLA SN Ia data, the constraints on the matter density shifts to Ωm = 0.313+0.042−0.040, bringing it into better agreement with the CMB cosmological parameter constraints from Planck. A positive Δ z∼ 2.6×10−4 would also cause us to overestimate the supernova measurement of Hubble's constant by Δ H0 ∼ 1 kms−1Mpc−1. However this overestimation should diminish as one increases the low-redshift cutoff, and this is not seen in the most recent data.

Constraining the dark energy equation of state using Bayes theorem and the Kullback–Leibler divergence

Data-driven model-independent reconstructions of the dark energy equation of state $w(z)$ are presented using Planck 2015 era CMB, BAO, SNIa and Lyman-$\alpha$ data. These reconstructions identify the $w(z)$ behaviour supported by the data and show a bifurcation of the equation of state posterior in the range $1.5{<}z{<}3$. Although the concordance $\Lambda$CDM model is consistent with the data at all redshifts in one of the bifurcated spaces, in the other a supernegative equation of state (also known as `phantom dark energy') is identified within the $1.5 \sigma$ confidence intervals of the posterior distribution. To identify the power of different datasets in constraining the dark energy equation of state, we use a novel formulation of the Kullback--Leibler divergence. This formalism quantifies the information the data add when moving from priors to posteriors for each possible dataset combination. The SNIa and BAO datasets are shown to provide much more constraining power in comparison to the Lyman-$\alpha$ datasets. Further, SNIa and BAO constrain most strongly around redshift range $0.1-0.5$, whilst the Lyman-$\alpha$ data constrains weakly over a broader range. We do not attribute the supernegative favouring to any particular dataset, and note that the $\Lambda$CDM model was favoured at more than $2$ log-units in Bayes factors over all the models tested despite the weakly preferred $w(z)$ structure in the data.

Application of Bayesian graphs to SN Ia data analysis and compression

Bayesian graphical models are an efficient tool for modelling complex data and derive self-consistent expressions of the posterior distribution of model parameters. We apply Bayesian graphs to perform statistical analyses of Type Ia supernova (SN Ia) luminosity distance measurements from the joint light-curve analysis (JLA) data set. In contrast to the $\chi^2$ approach used in previous studies, the Bayesian inference allows us to fully account for the standard-candle parameter dependence of the data covariance matrix. Comparing with $\chi^2$ analysis results, we find a systematic offset of the marginal model parameter bounds. We demonstrate that the bias is statistically significant in the case of the SN Ia standardization parameters with a maximal 6 $\sigma$ shift of the SN light-curve colour correction. In addition, we find that the evidence for a host galaxy correction is now only 2.4 $\sigma$. Systematic offsets on the cosmological parameters remain small, but may increase by combining constraints from complementary cosmological probes. The bias of the $\chi^2$ analysis is due to neglecting the parameter-dependent log-determinant of the data covariance, which gives more statistical weight to larger values of the standardization parameters. We find a similar effect on compressed distance modulus data. To this end, we implement a fully consistent compression method of the JLA data set that uses a Gaussian approximation of the posterior distribution for fast generation of compressed data. Overall, the results of our analysis emphasize the need for a fully consistent Bayesian statistical approach in the analysis of future large SN Ia data sets.

DETECTABILITY OF COSMIC DARK FLOW IN THE TYPE IA SUPERNOVA REDSHIFT–DISTANCE RELATION

ABSTRACT We reanalyze the detectability of large-scale dark flow (or local bulk flow) with respect to the CMB background based upon the redshift–distance relation for SN Ia. We made two independent analyses: one based upon identifying the three Cartesian velocity components; and the other based upon the cosine dependence of the deviation from Hubble flow on the sky. We apply these analyses to the Union2.1 SN Ia data and to the SDSS-II supernova survey. For both methods, results for low redshift, , are consistent with previous searches. We find a local bulk flow of v bf ∼ 300 km s−1 in the direction of (l, b) ∼ (270, 35)°. However, the search for a dark flow at is inconclusive. Based upon simulated data sets, we deduce that the difficulty in detecting a dark flow at high redshifts arises mostly from the observational error in the distance modulus. Thus, even if it exists, a dark flow is not detectable at large redshift with current SN Ia data sets. We estimate that a detection would require both significant sky coverage of SN Ia out to z = 0.3 and a reduction in the effective distance modulus error from 0.2 mag to ≲0.02 mag. We estimate that a greatly expanded data sample of ∼104 SN Ia might detect a dark flow as small as 300 km s−1 out to z = 0.3 even with a distance modulus error of 0.2 mag. This may be achievable in a next generation large survey like LSST.