Gaussian processes are used to reconstruct the dark energy equation of state from data.Gaussian process regression is a powerful statistical method for reconstructing a function from noisy data without assuming a specific parametric form. In cosmology, it is often used to infer the dark energy equation of state — the relationship between pressure and energy density that drives the accelerated expansion of the universe — directly from observations like supernova distances or cosmic microwave background data. Unlike traditional fitting with a fixed model (e.g., a constant or a polynomial), Gaussian processes allow the data to 'speak for themselves', providing a flexible, non-parametric reconstruction that also quantifies uncertainty at every point.
Gaussian processes for regression were formalized in the 1990s, building on earlier work in geostatistics (kriging) and machine learning. Their application to dark energy reconstruction became popular in the 2010s, with key papers by Holsclaw et al. and others.
The method treats the unknown function as a random draw from a Gaussian process — a collection of random variables, any finite subset of which has a joint Gaussian distribution. A covariance function (kernel) encodes assumptions about smoothness and correlation length. Given observed data points, the process is conditioned on them, yielding a posterior distribution over functions. The mean of this posterior gives the best estimate of the function, while the variance gives error bars that grow in regions with sparse data. The kernel hyperparameters are typically optimized by maximizing the marginal likelihood.
Beyond cosmology, Gaussian process regression is used in robotics for learning control policies, in weather forecasting for spatial interpolation, and in Bayesian optimization for tuning hyperparameters of machine learning models.
Gaussian processes are a cornerstone of modern Bayesian optimization, which is used to tune the Large Hadron Collider's magnet settings.









