2010
DOI: 10.1103/physrevd.82.103502
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Nonparametric reconstruction of the dark energy equation of state

Abstract: A basic aim of ongoing and upcoming cosmological surveys is to unravel the mystery of dark energy. In the absence of a compelling theory to test, a natural approach is to better characterize the properties of dark energy in search of clues that can lead to a more fundamental understanding. One way to view this characterization is the improved determination of the redshift-dependence of the dark energy equation of state parameter, w(z). To do this requires a robust and bias-free method for reconstructing w(z) f… Show more

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Cited by 137 publications
(139 citation statements)
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“…While there are several methods such as principle component analysis [19][20][21], Gaussian smoothing [22,23] and Gaussian processes [24][25][26][27], in this paper we will reconstruct D(z) and its derivatives more precisely by using the GP method.…”
Section: Reconstruction Methodsmentioning
confidence: 99%
“…While there are several methods such as principle component analysis [19][20][21], Gaussian smoothing [22,23] and Gaussian processes [24][25][26][27], in this paper we will reconstruct D(z) and its derivatives more precisely by using the GP method.…”
Section: Reconstruction Methodsmentioning
confidence: 99%
“…Based on the definition of a GP, one can assume that, for any collection z 1 , ..., z n , w(z 1 ), ..., w(z n ) follow a multivariate Gaussian distribution with a constant negative mean This new, nonparametric reconstruction method has the following advantages: it avoids artificial biases due to restricted parametric assumptions for w(z), it does not lose information about the data by smoothing it, and it does not introduce arbitrariness (and lack of error control) in reconstruction by representing the data using a certain number of bins, or cutting off information by using a restricted set of basis functions to represent the data [767,768]. In [767], using this reconstruction method, Holsclaw et al reconstructed w(z) utilizing the Constitution dataset [332].…”
Section: Gaussian Process Modelingmentioning
confidence: 99%
“…In practice, one must fit the distance data with a smooth function, and the fitting process introduces systematic biases. While a variety of methods have been pursued [Huterer and Turner (2001); Weller and Albrecht (2002)], it appears that direct reconstruction is too challenging and not robust even with SN Ia data of excellent quality (though see Holsclaw et al (2010)). And while the reconstruction of ρ DE (z) is easier since it involves only first derivatives of distance, w(z) is more useful a quantity since it contains more information about the nature of dark energy than ρ DE (z).…”
Section: Direct Reconstructionmentioning
confidence: 99%