Fundamental properties of protoplanetary discs determined from simultaneous fits to thermal dust images and spectral energy distributions

Abstract

We present a novel machine learning method that is capable of rapidly and accurately producing dust-continuum model images and spectral energy distributions from training sets created using a detailed radiative transfer code. We create a training set that encompasses the parameter space for protoplanetary discs, and then couple the trained machine learning method with a Bayesian optimisation algorithm. We then simultaneously fitted 1.3 mm ALMA ODISEA survey images of protostellar discs in the Ophiuchus Molecular Cloud, and their spectral energy distributions, in order to determine fundamental discs parameters such as dust masses and radii. We find that good simultaneous fits may be found for the Class II objects in the survey, although the spectral fits are poorer for the Class I and flat spectrum sources. We find that the dust mass distributions of discs is broader and shallower than that predicted from 1.3 mm flux dust mass estimates, substantially increasing the numbers of objects with high-mass and low-mass discs. We show that this is due to a combination of optical depth and dust temperature effects, which are strongly related to the disc size and inclination constraints provided by the imaging fits. We show that there is a significant decrease in disc scale height and disc flaring when moving from the the Class I objects, to the flat spectrum sources, and the Class II discs.