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Nearly a century ago, Edwin Hubble famously classified galaxies into three distinct groups: , spirals and irregulars (Hubble, 1926). Today, by analysing millions of galaxies with image processing techniques Astronomers have expanded on this picture and revealed rich diversity of galaxy morphology in both the nearby and distant Universe (Kormendy, 2015; Van Der Wel et al. , 2012; Vulcani et al. , 2014). PyAutoGalaxy is an open-source 3. 8+ package for analysing the morphologies and structures of large multiwavelength samples, with core features including fully automated Bayesian model-fitting of galaxy -dimensional surface brightness profiles, support for imaging and interferometer datasets comprehensive tools for simulating galaxy images. The software places a focus on big analysis, including support for hierarchical models that simultaneously fit thousands of, massively parallel model-fitting and an SQLite3 database that allows large suites modeling results to be loaded, queried and analysed. Accompanying PyAutoGalaxy is the workspace, which includes example scripts, datasets and the HowToGalaxy lectures Jupyter notebook format which introduce non-experts to studies of galaxy morphology using. Readers can try PyAutoGalaxy right now by going to the introduction Jupyter on Binder or checkout the readthedocs for a complete overview of PyAutoGalaxy’s.
Nightingale et al. (Fri,) studied this question.