A Census of Outflow to Magnetic Field Orientations in Nearby Molecular Clouds

TL;DR

This study investigates the relationship between protostellar outflow orientations and magnetic field directions in nearby molecular clouds, finding a preference for alignment but with significant dispersion due to various physical factors.

Contribution

It provides the first large-scale statistical analysis of outflow-magnetic field alignment and introduces a neural network method to estimate 3D outflow orientations.

Findings

Outflows tend to align with magnetic fields at ~30 degrees.

Alignment is significant but exhibits broad dispersion (~50 degrees).

Small-scale (less than 0.5 pc) outflow pairs show some alignment in 3D.

Abstract

We define a sample of 200 protostellar outflows showing blue and redshifted CO emission in the nearby molecular clouds Ophiuchus, Taurus, Perseus and Orion to investigate the correlation between outflow orientations and local, but relatively large-scale, magnetic field directions traced by Planck 353 GHz dust polarization. At high significance (p~1e-4), we exclude a random distribution of relative orientations and find that there is a preference for alignment of projected plane of sky outflow axes with magnetic field directions. The distribution of relative position angles peaks at ~30deg and exhibits a broad dispersion of ~50deg. These results indicate that magnetic fields have dynamical influence in regulating the launching and/or propagation directions of outflows. However, the significant dispersion around perfect alignment orientation implies that there are large measurement uncertainties and/or a high degree of intrinsic variation caused by other physical processes, such as turbulence or strong stellar dynamical interactions. Outflow to magnetic field alignment is expected to lead to a correlation in the directions of nearby outflow pairs, depending on the degree of order of the field. Analyzing this effect we find limited correlation, except on relatively small scales < 0.5 pc. Furthermore, we train a convolutional neural network to infer the inclination angle of outflows with respect to the line of sight and apply it to our outflow sample to estimate their full 3D orientations. We find that the angles between outflow pairs in 3D space also show evidence of small-scale alignment.