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While quiescent galaxies have comparable amounts of cool gas in their outer circumgalactic medium (CGM) compared to star-forming galaxies, they have significantly less interstellar gas. However, open questions remain on the processes causing galaxies to stop forming stars and stay quiescent. Theories suggest dynamical interactions with the hot corona prevent cool gas from reaching the galaxy, therefore predicting the inner regions of quiescent galaxy CGMs are devoid of cool gas. However, there is a lack of understanding of the inner regions of CGMs due to the lack of spatial information in quasar-sightline methods. We present integral-field spectroscopy probing 10–20 kpc (2.4–4.8 R e ) around a massive quiescent galaxy using a gravitationally lensed star-forming galaxy. We detect absorption from Magnesium (MgII) implying large amounts of cool atomic gas (10 8.4 –10 9.3 M ⊙ with T~10 4 Kelvin), in comparable amounts to star-forming galaxies. Lens modeling of Hubble imaging also reveals a diffuse asymmetric component of significant mass consistent with the spatial extent of the MgII absorption, and offset from the galaxy light profile. This study demonstrates the power of galaxy-scale gravitational lenses to not only probe the gas around galaxies, but to also independently probe the mass of the CGM due to it’s gravitational effect. Quiescent galaxies have similar amount of cool gas to star forming galaxies, yet why galaxies stop forming stars remains an open question. The authors investigate why passive galaxies remain quiescent using a gravitationally lensed background galaxy to probe the faint, diffuse cool gas around a massive quiescent galaxy, and use lensing configuration to constrain the total mass and geometry of this gas reservoir.

The distribution of mass in galaxy-scale strong gravitational lenses is often modelled as an elliptical power law plus ‘external shear’, which notionally accounts for line-of-sight galaxies and cosmic shear. We argue that it does not, using three lines of evidence from the analysis of 54 galaxy-scale strong lenses: (i) strong lensing external shears do not correlate with weak lensing; (ii) the measured shear magnitudes in strong lenses (which are field galaxies) are too large (exceeding 0.05) for their environment and; (iii) the external shear position angle preferentially aligns or anti-aligns with the mass model position angle, indicating an internal origin. We argue the measured strong lensing shears are therefore systematically accounting for missing complexity in the canonical elliptical power-law mass model. If we can introduce this complexity into our lens models, this will further lensing studies of galaxy formation, dark matter and Cosmology.

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 domain-specific knowledge of a problem via non-linear search chaining. Accompanying PyAutoFit is the autofit workspace (see https://github.com/Jammy2211/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 (see https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/HEAD) or checkout our readthedocs(see https://pyautofit.readthedocs.io/en/latest/) 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 \\(M_{\\rm BH}\\) and host galaxy velocity dispersion \\(\\sigma_{\\rm e}\\). Outside the local Universe, measurements of \\(M_{\\rm BH}\\) 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 \\(\\sim 1\\) kpc from the galaxy centre. Using multi-band Hubble Space Telescope imaging and the lens modeling software \\(\\texttt{PyAutoLens}\\) we reconstruct the distribution of mass along this line of sight. Bayesian model comparison favours a point mass with \\(M_{\\rm BH} = 3.27 \\pm 2.12\\times10^{10}\\,\\)M\\(_{\\rm \\odot}\\) (3\\(\\sigma\\) confidence limit); an ultramassive black hole. One model gives a comparable Bayesian evidence without a SMBH, however we argue this model is nonphysical given its base assumptions. This model still provides an upper limit of \\(M_{\\rm BH} \\leq 5.3 \\times 10^{10}\\,\\)M\\(_{\\rm \\odot}\\), because a SMBH above this mass deforms the lensed image \\(\\sim 1\\) kpc from Abell 1201's centre. This builds on previous work using central images to place upper limits on \\(M_{\\rm BH}\\), 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 \\(M_{\\rm BH}\\)--\\(\\sigma_{\\rm e}\\) relation. Results are available at https://github.com/Jammy2211/autolens_abell_1201.

The fast growth of supermassive black holes and their feedback to the host galaxies play an important role in regulating the evolution of galaxies, especially in the early Universe. However, due to cosmological dimming and the limited angular resolution of most observations, it is difficult to resolve the feedback from the active galactic nuclei (AGN) to their host galaxies. Gravitational lensing, for its magnification, provides a powerful tool to spatially differentiate emission originated from AGN and host galaxy at high redshifts. Here we report a discovery of a radio lobe in a strongly lensed starburst quasar, H1413+117 or Cloverleaf at redshift \\(z= 2.56\\), based on observational data at optical, sub-millimetre, and radio wavelengths. With both parametric and non-parametric lens models and with reconstructed images on the source plane, we find a differentially lensed, kpc scaled, single-sided radio lobe, located at \\({\\sim}1.2\\,\\mathrm{kpc}\\) to the north west of the host galaxy on the source plane. From the spectral energy distribution in radio bands, we find that the radio lobe has an energy turning point residing between 1.5 GHz and 8 GHz, indicating an age of 20--50 Myr. This could indicate a feedback switching of Cloverleaf quasar from the jet mode to the quasar mode.

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 (see https://github.com/Jammy2211/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 (see https://mybinder.org/v2/gh/Jammy2211/autolens_workspace/master) or checkout the readthedocs (see https://pyautolens.readthedocs.io/en/latest/) 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.

Observations suggest that the dark matter and stars in early-type galaxies `conspire' to produce a surprisingly simple distribution of total mass, \\(\\rho(r)\\propto\\rho^{-\\gamma}\\), with \\(\\gamma\\approx2\\). We measure the distribution of mass in 48 early-type galaxies that gravitationally lens a resolved background source. By fitting the source light in every pixel of images from the Hubble Space Telescope, we find a mean \\(\\langle\\gamma\\rangle=2.075_{-0.024}^{+0.023}\\) with intrinsic scatter between galaxies of \\(\\sigma_\\gamma=0.172^{+0.022}_{-0.032}\\) for the overall sample. This is consistent with, and has similar precision to traditional techniques that employ spectroscopic observations to supplement lensing with mass estimates from stellar dynamics. Comparing measurements of \\(\\gamma\\) for individual lenses using both techniques, we find a statistically insignificant correlation of \\(-0.150^{+0.223}_{-0.217}\\) between the two, indicating a lack of statistical power or deviations from a power-law density in certain lenses. At fixed surface mass density, we measure a redshift dependence, \\(\\partial\\langle\\gamma\\rangle/\\partial z=0.345^{+0.322}_{-0.296}\\), that is consistent with traditional techniques for the same sample of SLACS and GALLERY lenses. Interestingly, the consistency breaks down when we measure the dependence of \\(\\gamma\\) on the surface mass density of a lens galaxy. We argue that this is tentative evidence for an inflection point in the total-mass density profile at a few times the galaxy effective radius -- breaking the conspiracy.

The distribution of dark and luminous matter can be mapped around galaxies that gravitationally lens background objects into arcs or Einstein rings. New surveys will soon observe hundreds of thousands of galaxy lenses, and current, labour-intensive analysis methods will not scale up to this challenge. We instead develop a fully automatic, Bayesian method which we use to fit a sample of 59 lenses imaged by the Hubble Space Telescope in uniform conditions. We set out to \\textit{leave no lens behind} and focus on ways in which automated fits fail in a small handful of lenses, describing adjustments to the pipeline that allows us to infer accurate lens models. Our pipeline ultimately fits {\\em all} 59 lenses in our sample, with a high success rate key because catastrophic outliers would bias large samples with small statistical errors. Machine Learning techniques might further improve the two most difficult steps: subtracting foreground lens light and initialising a first, approximate lens model. After that, increasing model complexity is straightforward. We find a mean \\(\\sim1\\%\\) measurement precision on the measurement of the Einstein radius across the lens sample which {\\em does not degrade with redshift} up to at least \\(z=0.7\\) -- in stark contrast to other techniques used to study galaxy evolution, like stellar dynamics. Our \\texttt{PyAutoLens} software is open source, and is also installed in the Science Data Centres of the ESA Euclid mission.

We analyze two galaxy-scale strong gravitational lenses, SPT0418-47 and SPT2147-50, using JWST NIRCam imaging across multiple filters. To account for angular complexity in the lens mass distribution, we introduce multipole perturbations with orders \\(m=1, 3, 4\\). Our results show strong evidence for angular mass complexity in SPT2147, with multipole strengths of 0.3-1.7 \\(\\%\\) for \\(m=3, 4\\) and 2.4-9.5 \\(\\%\\) for \\(m=1\\), while SPT0418 shows no such preference. We also test lens models that include a dark matter substructure, finding a strong preference for a substructure in SPT2147-50 with a Bayes factor (log-evidence change) of \\(\\sim 60\\) when multipoles are not included. Including multipoles reduces the Bayes factor to \\(\\sim 11\\), still corresponding to a \\(5\\sigma\\) detection of a subhalo with an NFW mass of \\(\\log_{10}(M_{200}/M_{\\odot}) = 10.87\\substack{+0.53\\\ -0.71}\\). While SPT2147-50 may represent the fourth detection of a dark matter substructure in a strong lens, further analysis is needed to confirm that the signal is not due to systematics associated with the lens mass model.