IUCAA Preprints
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Item Smoothing supernova data to reconstruct the expansion history of the universe(2006-01-10) Shafieloo, Arman; Alam, Ujjaini; Sahni, VarunWe 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 ( < ∼ 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 Ω0m is marginalized over. The w-probe can be used to successfully distinguish between ΛCDM and other models of dark energy to a high degree of accuracy.Item Reconstructing cosmological matter perturbations using standard candles and rulers(2008-01-31) Alam, Ujjaini; Sahni, Varun; Starobinsky, A. A.For a large class of dark energy (DE) models, for which the effective gravitational constant is a constant and there is no direct exchange of energy between DE and dark matter (DM), knowledge of the expansion history suffices to reconstruct the growth factor of linearized density perturbations in the non-relativistic matter component on scales much smaller than the Hubble distance. In this paper we develop a nonparametric method for extracting information about the perturbative growth factor from data pertaining to the luminosity or angular size distances. A comparison of the reconstructed density contrast with observations of large scale structure and gravitational lensing can help distinguish DE models such as the cosmological constant and quintessence from models based on modified gravity theories as well as models in which DE and DM are either unified, or interact directly. We show that for current SNe data, the linear growth factor at z = 0.3 can be constrained to 5%, and the linear growth rate to 6%. With future SNe data, such as expected from the JDEM mission, we may be able to constrain the growth factor to 2−3% and the growth rate to 3−4% at z = 0.3 with this unbiased, model-independent reconstruction method. For future BAO data which would deliver measurements of both the angular diameter distance and Hubble parameter, it should be possible to constrain the growth factor at z = 2.5 to 9%. These constraints grow tighter with the errors on the datasets. With a large quantity of data expected in the next few years, this method can emerge as a competitive tool for distinguishing between different models of dark energy.