Searching for Molecular Outflows with Support Vector Machines: The Dark Cloud Complex in Cygnus

Abstract

Abstract We present a survey of molecular outflows across the dark cloud complex in the Cygnus region, based on a 46.75 deg2 field of CO isotopologue data from the Milky Way Imaging Scroll Painting survey. A supervised machine-learning algorithm, the support vector machine, is introduced to accelerate our visual assessment of outflow features in the data cube of 12CO and 13CO J = 1−0 emission. A total of 130 outflow candidates are identified, 77 of which show bipolar structures and 118 are new detections. Spatially, these outflows are located inside dense molecular clouds, and some of them are found in clusters or in elongated linear structures tracing the underlying gas filament morphology. Along the line of sight, 97, 31, and 2 candidates reside in the Local, Perseus, and Outer Arms, respectively. Young stellar objects as outflow drivers are found near most outflows, while 36 candidates show no associated source. The clusters of outflows that we detect are inhomogeneous in their properties; nevertheless, we show that the outflows cannot inject turbulent energy on cloud scales. Instead, at best, they are restricted to affecting the so-called “clump” and “core” scales, and only on short (∼0.3 Myr) estimated timescales. Combined with outflow samples in the literature, our work shows a tight outflow mass–size correlation.