VolDen: A tool to extract number density from the column density of filamentary molecular clouds

Abstract

Gas volume density is one of the critical parameters, along with dispersions in magnetic field position angles and non-thermal gas motions, for estimating the magnetic field strength using the Davis–Chandrasekhar–Fermi (DCF) relation or through its modified versions for a given region of interest. We present VolDen an novel python-based algorithm to extract the number density map from the column density map for an elongated interstellar filament. VolDen uses the workflow of RadFil to prepare the radial profiles across the spine. The user has to input the column density map and pre-computed spine along with the essential RadFil parameters (such as distance to the filament, the distance between two consecutive radial profile cuts, etc.) to extract the radial column density profiles. The thickness and volume density values are then calculated by modeling the column density profiles with a Plummer-like profile and introducing a cloud boundary condition. The cloud boundary condition was verified through an accompanying N-PDF column density analysis. In this paper, we discuss the workflow of VolDen and apply it to two filamentary clouds. We chose LDN1495 as our primary target owing to its nearby distance and elongated morphology. In addition, the distant filament RCW57A is selected as the secondary target to compare our results with the published results. Upon publication, a complete tutorial of VolDen and the codes will be available via https://github.com/aa16oaslak/volden.