A Comprehensive Approach to Resolving the Nature of the Dark Energy
Greg Huey
TL;DR
This work proposes ARDA, a data-driven, global framework to resolve the nature of dark energy by jointly analyzing a high-dimensional cosmological parameter space ($N \sim 20$) with diverse datasets. It introduces a covariant fluid perturbation approach that accommodates crossings of $w=-1$, a rest-frame sound speed $c_s^2$, and anisotropic stress $\Sigma$, all within an adaptable parameter space that can grow with data. The computational backbone relies on Importance Sampling with Kernel Density Estimation anchored to a CMB-based base distribution, enabling fast, modular inclusion of arbitrary experiments via reweighting. Through Fisher-matrix forecasts and a proof-of-concept web tool, the paper argues that many dark-energy parameters could be constrained in the near future, while highlighting the framework's extensibility, efficiency, and community-driven evolution.
Abstract
A data-driven approach to elucidating the nature of the dark energy, in the form of a joint analysis of a full set of cosmological parameters, utilizing all available observational data is proposed. A parameterization of a generalized dark energy is developed with the extension of fluid perturbation theory to models which cross through an equation of state of -1. This parameterization is selected to be general enough to admit a wide variety of behavior, while still being physical and economical. A Fisher matrix analysis with future high-precision CMB, cluster survey, and SNIa data suggests the parameters will probably be resolvable in the foreseeable future. How accurately the parameters can be determined depends sensitively on the nature of the dark energy - particularly how significant of a fraction of the total energy density it has been in the past. Parameter space will be sampled at a large number of points, with cosmological information such as CMB, power spectra, etc of each point being archived. Thus the likelihood functions of an arbitrary set of experiments can be applied to parameter space with insignificant new computational cost, making a wide variety of analyses possible. The resulting tool for Analysis and Resolution of Dark-sector Attributes, ARDA, will be highly versatile and adaptable. ARDA will allow the scientific community to extract parameters with an arbitrary set of experiments and theoretical priors, test for tension between classes of observations and investigate the effectiveness of hypothetical experiments, while evolving in a data-driven manner. A proof-of-concept prototype web-tool, \underbar{The Cosmic Concordance Project}, is already available.