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 determine the inner density profiles of massive galaxy clusters (M200 > 5 × 1014 M⊙) in the Cluster-EAGLE (C-EAGLE) hydrodynamic simulations, and investigate whether the dark matter density profiles can be correctly estimated from a combination of mock stellar kinematical and gravitational lensing data. From fitting mock stellar kinematics and lensing data generated from the simulations, we find that the inner density slopes of both the total and the dark matter mass distributions can be inferred reasonably well. We compare the density slopes of C-EAGLE clusters with those derived by Newman et al. for seven massive galaxy clusters in the local Universe. We find that the asymptotic best-fitting inner slopes of ‘generalized’ Navarro–Frenk–White (gNFW) profiles, γgNFW, of the dark matter haloes of the C-EAGLE clusters are significantly steeper than those inferred by Newman et al. However, the mean mass-weighted dark matter density slopes of the simulated clusters are in good agreement with the Newman et al. estimates. We also find that the estimate of γgNFW is very sensitive to the constraints from weak lensing measurements in the outer parts of the cluster and a bias can lead to an underestimate of γgNFW.

A defining prediction of the cold dark matter (CDM) cosmological model is the existence of a very large population of low-mass haloes. This population is absent in models in which the dark matter particle is warm (WDM). These alternatives can, in principle, be distinguished observationally because halos along the line-of-sight can perturb galaxy-galaxy strong gravitational lenses. Furthermore, the WDM particle mass could be deduced because the cut-off in their halo mass function depends on the mass of the particle. We systematically explore the detectability of low-mass haloes in WDM models by simulating and fitting mock lensed images. Contrary to previous studies, we find that halos are harder to detect when they are either behind or in front of the lens. Furthermore, we find that the perturbing effect of haloes increases with their concentration: detectable haloes are systematically high-concentration haloes, and accounting for the scatter in the mass-concentration relation boosts the expected number of detections by as much as an order of magnitude. Haloes have lower concentration for lower particle masses and this further suppresses the number of detectable haloes beyond the reduction arising from the lower halo abundances alone. Taking these effects into account can make lensing constraints on the value of the mass function cut-off at least an order of magnitude more stringent than previously appreciated.

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 develop an automatic, Bayesian method which we use to fit a sample of 59 lenses imaged by the Hubble Space Telescope. We set out to 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 ultimately allows us to infer accurate lens models for all 59 lenses. A high success rate is key to avoid catastrophic outliers that would bias large samples with small statistical errors. We establish the two most difficult steps to be subtracting foreground lens light and initialising a first, approximate lens model. After that, increasing model complexity is straightforward. We put forward a likelihood cap method to avoid the underestimation of errors due to pixel discretization noise inherent to pixel-based methods. With this new approach to error estimation, we find a mean $\sim 1{{\%}}$ fractional uncertainty on the Einstein radius measurement which does not degrade with redshift up to at least z = 0.7. This is in stark contrast to measurables from other techniques, like stellar dynamics, and demonstrates the power of lensing for studies of galaxy evolution. Our PyAutoLens software is open source, and is installed in the Science Data Centres of the ESA Euclid mission.

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The elliptical power-law (EPL) mass model of the mass in a galaxy is widely used in strong gravitational lensing analyses. However, the distribution of mass in real galaxies is more complex. We quantify the biases due to this model mismatch by simulating and then analysing mock {\it Hubble Space Telescope} imaging of lenses with mass distributions inferred from SDSS-MaNGA stellar dynamics data. We find accurate recovery of source galaxy morphology, except for a slight tendency to infer sources to be more compact than their true size. The Einstein radius of the lens is also robustly recovered with 0.1\% accuracy, as is the global density slope, with 2.5\% relative systematic error, compared to the 3.4\% intrinsic dispersion. However, asymmetry in real lenses also leads to a spurious fitted `external shear' with typical strength, $\gamma_{\rm ext}=0.015$. Furthermore, time delays inferred from lens modelling without measurements of stellar dynamics are typically underestimated by $\sim$5\%. Using such measurements from a sub-sample of 37 lenses would bias measurements of the Hubble constant $H_0$ by $\sim$9\%. The next generation cosmography must use more complex lens mass models.

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 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 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 ∼ 1% measurement precision on the measurement of the Einstein radius across the lens sample which 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 PyAutoLens software is open source, and is also installed in the Science Data Centres of the ESA Euclid mission.

A fundamental prediction of the cold dark matter (CDM) model of structure formation is the existence of a vast population of dark matter haloes extending to subsolar masses. By contrast, other dark matter models, such as a warm thermal relic (WDM), predict a cutoff in the mass function at a mass which, for popular models, lies approximately between 107 and 1010 M⊙. We use mock observations to demonstrate the viability of a forward modelling approach to extract information about low-mass dark haloes lying along the line-of-sight to galaxy-galaxy strong lenses. This can be used to constrain the mass of a thermal relic dark matter particle, mDM. With 50 strong lenses at Hubble Space Telescope resolution and a maximum pixel signal-to-noise ratio of ∼50, the expected median 2σ constraint for a CDM-like model (with a halo mass cutoff at 107 M⊙) is mDM > 4.10 keV (50% chance of constraining mDM to be better than 4.10 keV). If, however, the dark matter is a warm particle of mDM = 2.2 keV, our ‘Approximate Bayesian Computation’ method would result in a median estimate of mDM between 1.43 and 3.21 keV. Our method can be extended to the large samples of strong lenses that will be observed by future telescopes, and could potentially rule out the standard CDM model of cosmogony. To aid future survey design, we quantify how these constraints will depend on data quality (spatial resolution and integration time) as well as on the lensing geometry (source and lens redshifts).