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

TL;DR
This paper introduces a machine learning approach coupled with Bayesian optimization to rapidly fit protoplanetary disc models to observational data, revealing new insights into disc properties across different classes.
Contribution
A novel machine learning method combined with Bayesian optimization for simultaneous fitting of disc images and spectral energy distributions, improving parameter estimation accuracy.
Findings
Broader and shallower dust mass distribution than previous estimates.
Significant decrease in disc scale height and flaring from Class I to Class II objects.
Good fits achieved for Class II objects, poorer for Class I and flat spectrum sources.
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…
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