Introduction

- discussion of virial analyses, value of multi-cloud consistent analyses

Ronan's text which may be useful here:

Dense cores are the birthplaces of stars. Throughout our Galaxy, these relatively small but crucially important structures can be found inside of much larger molecular clouds. Observations of these clouds provide an opportunity to collect data on a large sample of cores at a variety of stages during the star formation process. As such, those data can be processed to answer questions about the dynamical states of the cores, and, by extension, shed light on the process of star formation. Virial analysis, which will be the focus of this paper, is an especially interesting metric, as it provides insight into what forces are dominant in the binding of these cores, and hence the evolution of the boundedness of the core population as a whole. Addressing both questions is important to broaden the our understanding of the physics of star formation.
The star-forming regions considered in this paper are NGC 1333 in Perseus, LDN 1688 in Ophiuchus (also referred to as L1688), and B18 in Taurus. They represent a sample with varied levels of activity, with very active star formation in L1688 and NGC 1333, in contrast to the much more quiescent B18 cloud. Differences in the rate of star formation will be likely reflected in the properties of the cloud, including temperature, line width, and column density. All these properties affect the propensity for core boundedness, and having a larger sample of regions allows us to probe a variety of dynamical situations. The data presented by the JCMT's Gould Belt Survey (GBS) \citep{WardThompson07} and the Green Bank radio telescope's (GBT) Green Bank Ammonia Survey (GAS) \citep{Friesen16} provide us with an opportunity to study the process of star formation in these regions with an unprecedented degree of detail. These regions represent three of the four released during GAS Data Release 1, making them the first clouds with high quality GBT ammonia data available.
Although many papers have performed a Virial Analysis on dense cores, the GAS data set represents a significant step forward, as both temperature and velocity dispersion across cores can be derived from simple ammonia emission maps. Such parameters are vital for formulating a strong estimate of the kinetic support being provided to a core. Unlike other tracers, ammonia is known for its presence at concentrations of greater than $10^3-10^4$ particles per cubic cm, which allows it to trace gas in much denser regions. The result is that rather than including the surrounding gas, the core itself is actively probed and measured. The result should be high quality, accurate thermal properties at the level of cores. When considering binding from gravity and pressure, accurate kinetic temperatures will be critical.
This paper is organized as follows: in \S ???, we introduce the data used in this paper, and walk through the derivation of important data properties that need to be derived prior to starting the analysis, such as the masses and radii of the cores. In \S ???, we perform a complete virial analysis, including the effects of gravity, pressure from the ambient cloud weight, and turbulent pressure as binding forces in opposition to internal kinetic support. With gravity alone, the vast majority of cores are shown to be unbound, while, with the addition of turbulent pressure, more than half of cores across all three regions become bound. In \S ???, we discuss our results, and the implications with respect to the role of turbulent pressure to star formation in the Gould Belt. Finally, we summarize our results and conclude in \S ???.

Methods

JCMT GBS observations

We use information from the James Clerk Maxwell Telescope (JCMT) Gould Belt Survey (GBS) to identify the dense cores for our virial analysis.  The JCMT GBS obtained large-area maps of dust continuum emission at 850~$\mu$m and 450~$\mu$m across all of the areas for which we have GAS kinematic data.  Here, we use the most recent maps available, from Data Release 2. [*refs for all of this*].  For our main analysis, we use the Getsources algorithm [*refs*] to identify dense cores.  Getsources allows users to identify compact structures using information available at multiple wavelengths, and we find it does a good job of identifying visually apparent dense cores in the three regions we analyze.  We discuss the details of the Getsources algorithm in Appendix ***, in addition to comparisons with a second core-identification algorithm, FellWalker, that we also ran.  In total, we identify *** cores in L1688, NGC 1333, and B18 respectively.  Figures ** to ** show the dense cores identified in each of the three regions.
For our virial analysis, we use the mass and total size of each core estimated by Getsources, in combination with kinematic properties estimated using the GAS data.  For the core total size, following \citep{Kirk17}, we adopt a core {\it radius} equal to the FWHM reported by Getsources, in order to measure the full core size.  For the core masses, we convert the total flux measured at 850~$\mu$m to a mass assuming the mean kinetic temperature of each core (see *GAS data section), using equation
[*type in flux to mass equation*]