Reconstruction of dark energy and expansion dynamics using Gaussian processes

By M. Seikel, C. Clarkson, Mathew Smith. 2012

413 citations recorded by the source corpus.

An important issue in cosmology is reconstructing the effective dark energy equation of state directly from observations. With few physically motivated models, future dark energy studies cannot only be based on constraining a dark energy parameter space, as the errors found depend strongly on the parameterisation considered. We present a new non-parametric approach to reconstructing the history of the expansion rate and dark energy using Gaussian Processes, which is a fully Bayesian approach for smoothing data. We present a pedagogical introduction to Gaussian Processes, and discuss how it can be used to robustly differentiate data in a suitable way. Using this method we show that the Dark Energy Survey - Supernova Survey (DES) can accurately recover a slowly evolving equation of state to sigma_w = +-0.04 (95% CL) at z=0 and +-0.2 at z=0.7, with a minimum error of +-0.015 at the sweet-spot at z~0.14, provided the other parameters of the model are known. Errors on the expansion history are an order of magnitude smaller, yet make no assumptions about dark energy whatsoever. A code for calculating functions and their first three derivatives using Gaussian processes has been developed and is available for download at this http URL .

Subfields: Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/008510b638ff50d1719fe468f45b4db64dfdcb39

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In Astronomy and Astrophysics

Citations in the corpus

Hubble Parameter and Baryon Acoustic Oscillation Measurement Constraints on the Hubble Constant, the Deviation from the Spatially Flat ΛCDM Model, the Deceleration–Acceleration Transition Redshift, and Spatial Curvature (2017, methodology): “In this work, we use the open-source Python package GaPP (Seikel et al. 2012a), which is widely used in cosmological studies (Bilicki & Seikel 2012; Seikel et al. 2012b; Cai et al. 2016; Wang et al. 2017a; Yu &Wang 2017).”
PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING (2016, methodology): “We use the package GaPP4 used in Seikel et al. (2012) to perform the Gaussian process regression of each of the light curves.”
Interpreting DESI's evidence for evolving dark energy (2024, positive): “Gaussian process modelling of w ( a ) [21, 22] would be one way to achieve this.”