The intrinsic shapes of starless cores in Ophiuchus

TL;DR

This study infers the three-dimensional shapes of starless cores in Ophiuchus from their projected aspect ratios using Bayesian analysis and compares several models to determine the most suitable description.

Contribution

It introduces multiple models for core shapes, applies Bayesian methods to fit observational data, and evaluates model merits to infer intrinsic core shapes.

Findings

Model M1 fits data well with σ_o ≈ 0.57

More complex models do not significantly improve fit

Cores are likely elongated or flattened, consistent with M1

Abstract

Using observations of cores to infer their intrinsic properties requires the solution of several poorly constrained inverse problems. Here we address one of these problems, namely to deduce from the projected aspect ratios of the cores in Ophiuchus their intrinsic three-dimensional shapes. Four models are proposed, all based on the standard assumption that cores are randomly orientated ellipsoids, and on the further assumption that a core's shape is not correlated with its absolute size. The first and simplest model, M1, has a single free parameter, and assumes that the relative axes of a core are drawn randomly from a log-normal distribution with zero mean and standard deviation \sigma o. The second model, M2a, has two free parameters, and assumes that the log-normal distribution (with standard deviation \sigma o) has a finite mean, \mu o, defined so that \mu o<0 means elongated (prolate) cores are favoured, whereas \mu o>0 means flattened (oblate) cores are favoured. Details of the third model (M2b, two free parameters) and the fourth model (M4, four free parameters) are given in the text. Markov chain Monte Carlo sampling and Bayesian analysis are used to map out the posterior probability density functions of the model parameters, and the relative merits of the models are compared using Bayes factors. We show that M1 provides an acceptable fit to the Ophiuchus data with \sigma o ~ 0.57+/-0.06; and that, although the other models sometimes provide an improved fit, there is no strong justification for the introduction of their additional parameters.