Inverse problem: Reconstruction of modified gravity action in Palatini formalism by Supernova Type Ia data

We introduce in $f(R)$ gravity--Palatini formalism the method of inverse problem to extract the action from the expansion history of the universe. First, we use an ansatz for the scale factor and apply the inverse method to derive an appropriate action for the gravity. In the second step we use the Supernova Type Ia data set from the Union sample and obtain a smoothed function for the Hubble parameter up to the redshift~1.7. We apply the smoothed Hubble parameter in the inverse approach and reconstruct the corresponding action in $f(R)$ gravity. In the next step we investigate the viability of reconstruction method, doing a Monte-Carlo simulation we generate synthetic SNIa data with the quality of union sample and show that roughly more than 1500 SNIa data is essential to reconstruct correct action. Finally with the enough SNIa data, we propose two diagnosis in order to distinguish between the $\Lambda$CDM model and an alternative theory for the acceleration of the universe.Comment: 8 pages, 8 figures, accepted in Phys. Rev.

Is cosmic acceleration slowing down?

We investigate the course of cosmic expansion in its `recent past' using the Constitution SN Ia sample (which includes CfA data at low redshifts), jointly with signatures of baryon acoustic oscillations (BAO) in the galaxy distribution and fluctuations in the cosmic microwave background (CMB). Earlier SN Ia data sets could not address this issue because of a paucity of data at low redshifts. Allowing the equation of state of dark energy (DE) to vary, we find that a coasting model of the universe (q_0=0) fits the data about as well as LCDM. This effect, which is most clearly seen using the recently introduced `Om' diagnostic, corresponds to an increase of Om(z) and q(z) at redshifts z \lleq 0.3. In geometrical terms, this suggests that cosmic acceleration may have already peaked and that we are currently witnessing its slowing down. The case for evolving DE strengthens if a subsample of the Constitution set consisting of SNLS+ESSENCE+CfA SN Ia data is analysed in combination with BAO+CMB using the same statistical methods. The effect we observe could correspond to DE decaying into dark matter (or something else). A toy model which mimics this process agrees well with the combined SN Ia+BAO+CMB data.Comment: 6 pages, 5 figures, presentation expanded, results for a new subsample of the Constitution set are added, new BAO data are accounted for, main results unchange

Two new diagnostics of dark energy

We introduce two new diagnostics of dark energy (DE). The first, Om, is a combination of the Hubble parameter and the cosmological redshift and provides a "null test" of dark energy being a cosmological constant. Namely, if the value of Om(z) is the same at different redshifts, then DE is exactly cosmological constant. The slope of Om(z) can differentiate between different models of dark energy even if the value of the matter density is not accurately known. For DE with an unevolving equation of state, a positive slope of Om(z) is suggestive of Phantom (w < -1) while a negative slope indicates Quintessence (w > -1). The second diagnostic, "acceleration probe"(q-probe), is the mean value of the deceleration parameter over a small redshift range. It can be used to determine the cosmological redshift at which the universe began to accelerate, again without reference to the current value of the matter density. We apply the "Om" and "q-probe" diagnostics to the Union data set of type Ia supernovae combined with recent data from the cosmic microwave background (WMAP5) and baryon acoustic oscillations.Comment: 14 pages, 9 figures. Some new results and an additional reference. Main conclusions unchanged. Matches published versio

A model-independent dark energy reconstruction scheme using the geometrical form of the luminosity-distance relation

We put forward a new model-independent reconstruction scheme for dark energy which utilises the expected geometrical features of the luminosity-distance relation. The important advantage of this scheme is that it does not assume explicit ansatzes for cosmological parameters but only some very general cosmological properties via the geometrical features of the reconstructed luminosity-distance relation. Using the recently released supernovae data by the Supernova Legacy Survey together with a phase space representation, we show that the reconstructed luminosity-distance curves best fitting the data correspond to a slightly varying dark energy density with the Universe expanding slightly slower than the Lambda CDM model. However, the Lambda CDM model fits the data at 1 sigma significance level and the fact that our best fitting luminosity-distance curve is lower than that of the corresponding Lambda CDM model could be due to systematics. The transition from an accelerating to a decelerating expansion occurs at a redshift larger than z=0.35. Interpreting the dark energy as a minimally coupled scalar field we also reconstruct the scalar field and its potential. We constrain $\Omega_{m_0}$ using the baryon acoustic oscillation peak in the SDSS luminous red galaxy sample and find that the best fit is obtained with $\Omega_{m_0}=0.27$, in agreement with the CMB data.Comment: 10 pages, 18 figure

High-resolution temporal constraints on the dynamics of dark energy

We use the recent type Ia supernova, cosmic microwave background and large-scale structure data to shed light on the temporal evolution of the dark energy equation of state $w(z)$ out to redshift one. We constrain the most flexible parametrization of dark energy to date, and include the dark energy perturbations consistently throughout. Interpreting our results via the principal component analysis, we find no significant evidence for dynamical dark energy: the cosmological constant model is consistent with data everywhere between redshift zero and one at 95% C.L.Comment: 5 pages, 2 figures Version for PRD (Rapid Communications

Smoothing Supernova Data to Reconstruct the Expansion History of the Universe and its Age

We propose a non-parametric method of smoothing supernova data over redshift using a Gaussian kernel in order to reconstruct important cosmological quantities including H(z) and w(z) in a model independent manner. This method is shown to be successful in discriminating between different models of dark energy when the quality of data is commensurate with that expected from the future SuperNova Acceleration Probe (SNAP). We find that the Hubble parameter is especially well-determined and useful for this purpose. The look back time of the universe may also be determined to a very high degree of accuracy (\lleq 0.2 %) in this method. By refining the method, it is also possible to obtain reasonable bounds on the equation of state of dark energy. We explore a new diagnostic of dark energy-- the `w-probe'-- which can be calculated from the first derivative of the data. We find that this diagnostic is reconstructed extremely accurately for different reconstruction methods even if \Omega_m is marginalized over. The w-probe can be used to successfully distinguish between $\Lambda$CDM and other models of dark energy to a high degree of accuracy.Comment: 16 pages, 12 figures. Section 5 restructured, main conclusions unchanged. Post journal publication versio

Crossing Statistic: Bayesian interpretation, model selection and resolving dark energy parametrization problem

By introducing Crossing functions and hyper-parameters I show that the Bayesian interpretation of the Crossing Statistics [1] can be used trivially for the purpose of model selection among cosmological models. In this approach to falsify a cosmological model there is no need to compare it with other models or assume any particular form of parametrization for the cosmological quantities like luminosity distance, Hubble parameter or equation of state of dark energy. Instead, hyper-parameters of Crossing functions perform as discriminators between correct and wrong models. Using this approach one can falsify any assumed cosmological model without putting priors on the underlying actual model of the universe and its parameters, hence the issue of dark energy parametrization is resolved. It will be also shown that the sensitivity of the method to the intrinsic dispersion of the data is small that is another important characteristic of the method in testing cosmological models dealing with data with high uncertainties.Comment: 14 pages, 4 figures, discussions extended, 1 figure and two references added, main results unchanged, matches the final version to be published in JCA