Beetroots: spatially-regularized Bayesian inference of physical parameter maps -- Application to Orion

Abstract

The current generation of millimeter receivers is able to produce cubes of 800 000 pixels by 200 000 frequency channels to cover several square degrees over the 3 mm atmospheric window. Estimating the physical conditions of the interstellar medium (ISM) with an astrophysical model on such datasets is challenging. Common approaches tend to converge to local minima and typically poorly reconstruct regions with low signal-to-noise ratio (S/N). This instrumental revolution thus calls for new scalable data analysis techniques. We present Beetroots, a Python software that performs Bayesian reconstruction of maps of physical conditions from observation maps and an astrophysical model. It relies on an accurate statistical model, exploits spatial regularization to guide estimations, and uses state-of-the-art algorithms. It also assesses the ability of the astrophysical model to explain the observations, providing feedback to improve ISM models. We demonstrate the power of Beetroots with the Meudon PDR code on synthetic data, and then apply it to estimate physical condition maps in the full Orion molecular cloud 1 (OMC-1) star forming region based on Herschel molecular line emission maps. The application to the synthetic case shows that Beetroots can currently analyse maps with up to ten thousand pixels, addressing large variations of S/N, escaping from local minima, and providing consistent uncertainty quantifications. On a laptop, the inference runtime ranges from a few minutes for 100-pixel maps to 28 hours for 8100-pixel maps. The results on the OMC-1 maps are consistent with independent estimations from the literature, and improve our understanding of the region. This work paves the way towards systematic and rigorous analyses of observations produced by current and future instruments.