Abstract: Large-scale surveys have brought about a revolution in astronomy. To analyse the resulting wealth of data, we need automated tools to identify, classify, and quantify the important underlying structures. We present a method for classifying and quantifying a pixelated structure, based on its principal moments of inertia. The method enables us to automatically detect, and objectively compare, centrally condensed cores, elongated filaments, and hollow rings. A Python implementation of this tool is available on GitHub to analyse 2D or 3D datasets, enabling an unbiased analysis and comparison of simulated and observed structures.
Credit: Jaffa, Sarah E.; Whitworth, Anthony P.; Clarke, Seamus D.
https://ui.adsabs.harvard.edu/abs/2018M ... J/abstract