Gaussian processes

Astrophysics

Gaussian processes have also found extensive use in astrophysical and astronomical settings. Gaussian processes can model correlated noise, a specific type of non-Gaussian noise dependent on some underlying unknown distribution correlated with observed values. This type of noise is often present in astronomical signals as instrumental systematics or as intrinsic to the observed object as a result of physical processes. Correlated noise is often a consideration for exoplanet transit events, and Gaussian processes have been used to de-trend these transit light curves (at timescales greater than that of the transit) to allow detection of weaker, more short-lived signals. These processes have also been used to disentangle planetary signals from stellar activity indicators inside radial velocity data, another method of exoplanet detection. This is done by training the Gaussian process model to optimize the hyperparameters of the kernel until it accurately recreates the noise components of the radial velocity data, which ultimately allows it to determine which signals are best defined as strictly periodic (which the planet should be) and which signals are best represented by the evolving, quasi-periodic kernel (which the star should be). Correlated noise produced by active regions on a star's photosphere (as a result of magnetic field interactions) can be of similar timescales as transit events, and Gaussian process models which handle sparsely sampled data are used to confirm exoplanet detections especially around young stars.