Volume Density Mapper: 3D Density Reconstruction Algorithm for Molecular Clouds

Abstract: The interstellar medium (ISM) exhibits complex, multi-scale structures that are challenging to study due to their projection into two-dimensional (2D) column density maps. We present the Volume Density Mapper, a novel algorithm based on constrained diffusion to reconstruct three-dimensional (3D) density distributions of molecular clouds from 2D observations. This method decomposes the column density into multi-scale components, reconstructing a 3D density field that preserves key physical properties such as mean density, maximum density, and standard deviation along the line of sight. Validated against numerical simulations (FLASH and ENZO), the algorithm achieves high accuracy, with mean density estimates within 0.1 dex and dispersions of 0.2 to 0.3 dex across varied cloud structures. The reconstructed 3D density fields enable the derivation of critical parameters, including volume density, cloud thickness, and density probability distribution functions, offering insights into star formation and ISM evolution. The versatility of the method is demonstrated by applying diverse systems from galaxies (NGC 628) to protostellar disks. The code is available at https://github.com/gxli/volume-density-mapper.