PyAutoGalaxy: Open-Source Multiwavelength GalaxyStructure & Morphology
J.W Nightingale, Aristeidis Amvrosiadis, Richard G. Hayes, Qiuhan He, Amy Etherington, Xiaoyue Cao, Shaun Cole, Jonathan Frawley, Carlos S. Frenk, Sam Lange, Ran Li, R. Massey, M. Negrello, Andrew Robertson
Abstract
Nearly a century ago, Edwin Hubble famously classified galaxies into three distinct groups: \nellipticals, spirals and irregulars (Hubble, 1926). Today, by analysing millions of galaxies with \nadvanced image processing techniques Astronomers have expanded on this picture and revealed \nthe rich diversity of galaxy morphology in both the nearby and distant Universe (Kormendy, \n2015; Van Der Wel et al., 2012; Vulcani et al., 2014). PyAutoGalaxy is an open-source \nPython 3.8+ package for analysing the morphologies and structures of large multiwavelength \ngalaxy samples, with core features including fully automated Bayesian model-fitting of galaxy \ntwo-dimensional surface brightness profiles, support for imaging and interferometer datasets \nand comprehensive tools for simulating galaxy images. The software places a focus on big \ndata analysis, including support for hierarchical models that simultaneously fit thousands of \ngalaxies, massively parallel model-fitting and an SQLite3 database that allows large suites \nof modeling results to be loaded, queried and analysed. Accompanying PyAutoGalaxy is the \nautogalaxy workspace, which includes example scripts, datasets and the HowToGalaxy lectures \nin Jupyter notebook format which introduce non-experts to studies of galaxy morphology using \nPyAutoGalaxy. Readers can try PyAutoGalaxy right now by going to the introduction Jupyter \nnotebook on Binder or checkout the readthedocs for a complete overview of PyAutoGalaxy’s \nfeatures.