Nonparametric reconstruction of the dark energy equation of state from diverse data sets

Abstract

The cause of the accelerated expansion of the Universe poses one of the most fundamental questions in physics today. In the absence of a compelling theory to explain the observations, a first task is to develop a robust phenomenological approach: If the acceleration is driven by some form of dark energy, then, the phenomenology is determined by the form of the dark energy equation of state w(z) as a function of redshift. A major aim of ongoing and upcoming cosmological surveys is to measure w and its evolution at high accuracy. Since w(z) is not directly accessible to measurement, powerful reconstruction methods are needed to extract it reliably from observations. We have recently introduced a new reconstruction method for w(z) based on Gaussian process modeling. This method can capture nontrivial w(z) dependences and, most importantly, it yields controlled and unbiased error estimates. In this paper we extend the method to include a diverse set of measurements: baryon acoustic oscillations, cosmic microwave background measurements, and supernova data. We analyze currently available data sets and present the resulting constraints on w(z), finding that current observations are in very good agreement with a cosmological constant. In addition we explore how well our method captures nontrivial behavior of w(z) by analyzing simulated data assuming high-quality observations from future surveys. We find that the baryon acoustic oscillation measurements by themselves already lead to remarkably good reconstruction results and that the combination of different high-quality probes allows us to reconstruct w(z) very reliably with small error bounds.

Model independent tests of cosmic growth versus expansion

“…Gaussian processes provide a robust statistical method for using stochastic data measured at certain points (redshifts) and reconstructing the full function (distanceredshift relation or inverse Hubble parameter) describing the underlying relation, complete with covariances and without assuming a specific model for the relation. See [1] for detailed explanation of their general application, and [2][3][4][5] for specific application to dark energy and cosmology (also see [6] for a genetic algorithm approach). Here we follow most closely [2].…”

Reconstructing the interaction between dark energy and dark matter using Gaussian processes

“…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.…”

Null tests of the cosmological constant using supernovae

“…To do this, we use Gaussian Processes (GP), which have previously been used to reconstruct w(z) from SNIa luminosity distances [22][23][24][25][26]. Our analysis is built on [14], which used H(z) data from the baryon acoustic oscillation (BAO) scale and galaxy ages to test the validity of the concordance model.…”