Strong gravitational lensing, which can make a background source galaxy appears multiple times due to its light rays being deflected by the mass of one or more foreground lens galaxies, provides astronomers with a powerful tool to study dark matter, cosmology and the most distant Universe. PyAutoLens is an open-source Python 3.6+ package for strong gravitational lensing, with core features including fully automated strong lens modeling of galaxies and galaxy clusters, support for direct imaging and interferometer datasets and comprehensive tools for simulating samples of strong lenses. The API allows users to perform ray-tracing by using analytic light and mass profiles to build strong lens systems. Accompanying PyAutoLens is the autolens workspace, which includes example scripts, lens datasets and the HowToLens lectures in Jupyter notebook format which introduce non-experts to strong lensing using PyAutoLens. Readers can try PyAutoLens right now by going to the introduction Jupyter notebook on Binder or checkout the readthedocs for a complete overview of PyAutoLens's features.

We investigate how strong gravitational lensing can test contemporary models of massive elliptical (ME) galaxy formation, by combining a traditional decomposition of their visible stellar distribution with a lensing analysis of their mass distribution. As a proof of concept, we study a sample of three ME lenses, observing that all are composed of two distinct baryonic structures, a 'red' central bulge surrounded by an extended envelope of stellar material. Whilst these two components look photometrically similar, their distinct lensing effects permit a clean decomposition of their mass structure. This allows us to infer two key pieces of information about each lens galaxy: (i) the stellar mass distribution (without invoking stellar populations models) and (ii) the inner dark matter halo mass. We argue that these two measurements are crucial to testing models of ME formation, as the stellar mass profile provides a diagnostic of baryonic accretion and feedback whilst the dark matter mass places each galaxy in the context of LCDM large scale structure formation. We also detect large rotational offsets between the two stellar components and a lopsidedness in their outer mass distributions, which hold further information on the evolution of each ME. Finally, we discuss how this approach can be extended to galaxies of all Hubble types and what implication our results have for studies of strong gravitational lensing.

A major trend in academia and data science is the rapid adoption of Bayesian statistics for data analysis and modeling, leading to the development of probabilistic programming languages (PPL). A PPL provides a framework that allows users to easily specify a probabilistic model and perform inference automatically. PyAutoFit is a Python-based PPL which interfaces with all aspects of the modeling (e.g., the model, data, fitting procedure, visualization, results) and therefore provides complete management of every aspect of modeling. This includes composing high-dimensionality models from individual model components, customizing the fitting procedure and performing data augmentation before a model-fit. Advanced features include database tools for analysing large suites of modeling results and exploiting domainspecific knowledge of a problem via non-linear search chaining. Accompanying PyAutoFit is the autofit workspace, which includes example scripts and the HowToFit lecture series which introduces non-experts to model-fitting and provides a guide on how to begin a project using PyAutoFit. Readers can try PyAutoFit right now by going to the introduction Jupyter notebook on Binder or checkout our readthedocs for a complete overview of PyAutoFit's features.

Supermassive black holes (SMBHs) are a key catalyst of galaxy formation and evolution, leading to an observed correlation between SMBH mass MBH and host galaxy velocity dispersion σe. Outside the local Universe, measurements of MBH are usually only possible for SMBHs in an active state: limiting sample size and introducing selection biases. Gravitational lensing makes it possible to measure the mass of non-active SMBHs. We present models of the $z$ = 0.169 galaxy-scale strong lens Abell 1201. A cD galaxy in a galaxy cluster, it has sufficient ‘external shear’ that a magnified image of a $z$ = 0.451 background galaxy is projected just ∼1 kpc from the galaxy centre. Using multiband Hubble Space Telescope imaging and the lens modelling software PYAUTOLENS, we reconstruct the distribution of mass along this line of sight. Bayesian model comparison favours a point mass with MBH = 3.27 ± 2.12 × 1010 M⊙ (3σ confidence limit); an ultramassive black hole. One model gives a comparable Bayesian evidence without an SMBH; however, we argue this model is nonphysical given its base assumptions. This model still provides an upper limit of MBH ≤ 5.3 × 1010 M⊙, because an SMBH above this mass deforms the lensed image ∼1 kpc from Abell 1201’s centre. This builds on previous work using central images to place upper limits on MBH, but is the first to also place a lower limit and without a central image being observed. The success of this method suggests that surveys during the next decade could measure thousands more SMBH masses, and any redshift evolution of the MBH−σe relation. Results are available at https://github.com/Jammy2211/autolens_abell_1201.

Nearly a century ago, Edwin Hubble famously classified galaxies into three distinct groups: ellipticals, spirals and irregulars (Hubble, 1926). Today, by analysing millions of galaxies with advanced image processing techniques Astronomers have expanded on this picture and revealed the 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 Python 3.8+ package for analysing the morphologies and structures of large multiwavelength galaxy samples, with core features including fully automated Bayesian model-fitting of galaxy two-dimensional surface brightness profiles, support for imaging and interferometer datasets and comprehensive tools for simulating galaxy images. The software places a focus on big data analysis, including support for hierarchical models that simultaneously fit thousands of galaxies, massively parallel model-fitting and an SQLite3 database that allows large suites of modeling results to be loaded, queried and analysed. Accompanying PyAutoGalaxy is the autogalaxy workspace, which includes example scripts, datasets and the HowToGalaxy lectures in Jupyter notebook format which introduce non-experts to studies of galaxy morphology using PyAutoGalaxy. Readers can try PyAutoGalaxy right now by going to the introduction Jupyter notebook on Binder or checkout the readthedocs for a complete overview of PyAutoGalaxy's features.