Nonparametric Reconstruction of the Dark Energy Equation of State from Diverse Data Sets
Tracy Holsclaw, Ujjaini Alam, Bruno Sanso, Herbie Lee, Katrin, Heitmann, Salman Habib, David Higdon

TL;DR
This paper introduces an advanced Gaussian process-based method for reconstructing the dark energy equation of state from diverse cosmological data, demonstrating its effectiveness with current and simulated future observations.
Contribution
It extends a Gaussian process reconstruction method to incorporate multiple data types, providing reliable, unbiased estimates of w(z) and its potential nontrivial behavior.
Findings
Current data agree with a cosmological constant.
BAO measurements alone yield good reconstruction results.
Combining multiple high-quality probes improves accuracy and reliability.
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 phenomenology. If the acceleration is driven by some form of dark energy, then, the phenomenology is determined by the dark energy equation of state w. A major aim of ongoing and upcoming cosmological surveys is to measure w and its time dependence 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 time-dependences in w(z) and, most importantly, it yields controlled and unbaised error estimates. In this paper we extend the method to…
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