Spatial field reconstruction with INLA

Abstract

Aims. Monte Carlo radiative transfer (MCRT) simulations are a powerful tool for understanding the role of dust in astrophysical systems and its influence on observations. However, due to the strong coupling of the radiation field and medium across the whole computational domain, the problem is non-local and non-linear, and such simulations are computationally expensive in the case of realistic 3D inhomogeneous dust distributions. We explore a novel technique for post-processing MCRT output to reduce the total computational run time by enhancing the output of computationally less expensive simulations of lower-quality. Methods. We combined principal component analysis (PCA) and non-negative matrix factorisation (NMF) as dimensionality reduction techniques together with Gaussian Markov random fields and the integrated nested Laplace approximation (INLA), an approximate method for Bayesian inference, to detect and reconstruct the non-random spatial structure in the images of lower signal-to-noise ratios or with missing data. Results. We tested our methodology using synthetic observations of a galaxy from the SKIRT Auriga project - a suite of high-resolution magnetohydrodynamic Milky Way-sized galaxies simulated in cosmological environment using a ‘zoom-in' technique. With this approach, we are able to reproduce high-photon-number reference images ~5 times faster with median residuals below ~20%.