A Census of Protostellar Outflows in Nearby Molecular Clouds

TL;DR

This study employs deep learning to systematically identify and analyze protostellar outflows in nearby molecular clouds, revealing their mass, energy, and impact on cloud turbulence, with implications for star formation processes.

Contribution

It introduces the CASI-3D deep learning method for large-scale outflow identification and provides comprehensive measurements across four molecular clouds, advancing understanding of outflow properties.

Findings

Outflow masses range from 267 to 6332 solar masses across clouds.

Outflow energies are sufficient to offset turbulent dissipation.

A break point in the spatial power spectrum indicates typical outflow scales.

Abstract

We adopt the deep learning method CASI-3D (Convolutional Approach to Structure Identification-3D) to systemically identify protostellar outflows in 12CO and 13CO observations of the nearby molecular clouds, Ophiuchus, Taurus, Perseus and Orion. The total outflow masses are 267 Msun, 795 Msun, 1305 Msun and 6332 Msun for Ophiuchus, Taurus, Perseus and Orion, respectively. We show the outflow mass in each cloud is linearly proportional to the total number of young stellar objects. The estimated total 3D deprojected outflow energies are 9e45 ergs, 6e46 ergs, 1.2e47 ergs and 6e47 ergs for Ophiuchus, Taurus, Perseus and Orion, respectively. The energy associated with outflows is sufficient to offset turbulent dissipation at the current epoch for all four clouds. All clouds also exhibit a break point in the spatial power spectrum of the outflow prediction map, which likely corresponds to the typical outflow mass and energy injection scale.