Speaker
Description
To reveal the nature of high-energy, gamma-ray sources and identify the associated accelerator and production mechanisms, we need detailed models capable of reproducing observed energy spectra and morphologies. To produce these gamma-ray models in a hadronic scenario, we need cosmic-ray and ISM distributions in 3D, as the gamma-ray morphology is sensitive to the relative distances between the accelerator and ISM gas clouds. However, these distances are typically not known with the precision we require. Our novel approach is to iterate over the distances of individual ISM clouds. To facilitate this, the 3D pixels belonging to a specific cloud structure are identified using clustering.
In this contribution, we will present results from our 3D molecular hydrogen distributions of ISM cloud structures, derived over the entire Mopra Southern Galactic Plane CO Survey. We will introduce our novel cloud identification method used to create these distributions. This involves Gaussian decompositions and clustering, as well as combining measurements from different gas tracers ($^{12}$CO and $^{13}$CO). We will also demonstrate how iterating over the physical distances to the cloud structures in our ISM maps, can significantly improve gamma-ray modelling.
