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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., Comment: 14 pages, 11 figures, 5 tables, submitted to MNRAS

This repository contains result and scripts to accompany the paper "Scanning For Dark Matter Subhalos in Hubble Space Telescope Imaging of 54 Strong Lenses". A full description of the data formats, and how to use them, can be found at the accompanying GitHub repository:https://github.com/Jammy2211/autolens_subhalo

We determine the inner density profiles of massive galaxy clusters (M$_{200}$ > $5 \times 10^{14}$ M$_{\odot}$) 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 7 massive galaxy clusters in the local Universe. We find that the asymptotic best-fit inner slopes of "generalized" NFW (gNFW) profiles, ${\gamma}_{\rm gNFW}$, 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 ${\gamma}_{\rm gNFW}$ 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 ${\gamma}_{\rm gNFW}$., Comment: 19 pages, 16 figures, accepted by MNRAS

Strong gravitational lensing offers a compelling test of the cold dark matter paradigm, as it allows for subhaloes with masses of $\sim10^{9}$ M$_\odot$ and below to be detected. We test commonly-used techniques for detecting subhaloes superposed in images of strongly lensed galaxies. For the lens we take a simulated galaxy in a $\sim10^{13}$ M$_\odot$ halo grown in a high-resolution cosmological hydrodynamical simulation, which we view from two different directions. Though the resolution is high, we note the simulated galaxy still has an artificial core which adds additional complexity to the baryon dominated region. To remove particle noise, we represent the projected galaxy mass distribution by a series of Gaussian profiles which precisely capture the features of the projected galaxy. We first model the lens mass as a (broken) power-law density profile and then search for small haloes. Of the two projections, one has a regular elliptical shape, while the other has distinct deviations from an elliptical shape. For the former, the broken power-law model gives no false positives and correctly recovers the mass of the superposed small halo, but for the latter we find false positives and the inferred halo mass is overestimated by $\sim4-5$ times. We then use a more complex model in which the lens mass is decomposed into stellar and dark matter components. In this case, we show that we can capture the simulated galaxy's complex projected structures and correctly infer the input small halo., Accepted by MNRAS. Comments welcome!

The elliptical power-law (EPL) 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%. Although this work is based on a particular set of MaNGA galaxies, and the specific value of the detected biases may change for another set of strong lenses, our results strongly suggest the next generation cosmography needs to use more complex lens mass models., Comment: Accepted by RAA; Fix a few typos, add some references

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., Comment: 23 pages, 20 figures, 5 tables. Submitted to MNRAS

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., MNRAS submitted. Comments welcome

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., Comment: 2 pages, 1 figure

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 $10^7$ and $10^{10}~{\rm M}_\odot$. 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, $m_\mathrm{DM}$. With 50 strong lenses at Hubble Space Telescope resolution and a maximum pixel signal-to-noise ratio of $\sim50$, the expected median 2$\sigma$ constraint for a CDM-like model (with a halo mass cutoff at $10^{7}~{\rm M}_\odot$) is $m_\mathrm{DM} > 4.10 \, \mathrm{keV}$ (50% chance of constraining $m_{\rm DM}$ to be better than 4.10 keV). If, however, the dark matter is a warm particle of $m_\mathrm{DM}=2.2 \, \mathrm{keV}$, our 'Approximate Bayesian Computation' method would result in a median estimate of $m_\mathrm{DM}$ 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)., Comment: Accepted by MNRAS. Comments welcome

Small distortions in the images of Einstein rings or giant arcs offer the exciting prospect of detecting dark matter haloes or subhaloes of mass below $10^9$M$_{\odot}$, most of which are too small to have made a visible galaxy. A very large number of such haloes are predicted to exist in the cold dark matter model of cosmogony; in contrast other models, such as warm dark matter, predict no haloes below a mass of this order which depends on the properties of the warm dark matter particle. Attempting to detect these small perturbers could therefore discriminate between different kinds of dark matter particles, and even rule out the cold dark matter model altogether. Globular clusters in the lens galaxy also induce distortions in the image which could, in principle, contaminate the test. Here, we investigate the population of globular clusters in six early type galaxies in the Virgo cluster. We find that the number density of globular clusters of mass $\sim10^6$M$_{\odot}$ is comparable to that of the dark matter perturbers (including subhaloes in the lens and haloes along the line-of-sight). We show that the very different degrees of mass concentration in globular clusters and dark matter haloes result in different lensing distortions. These are detectable with milli-arcsecond resolution imaging which can distinguish between globular cluster and dark matter halo signals., Comment: 6 pages, 4 figures, submitted to MNRAS

We rederive the number density of intervening line-of-sight haloes relative to lens subhaloes in galaxy-galaxy strong lensing observations, where these perturbers can generate detectable image fluctuations. Previous studies have calculated the detection limit of a line-of-sight small-mass dark halo by comparing the lensing deflection angles it would cause, to those caused by a subhalo within the lens. However, this overly simplifies the difference in observational consequences between a subhalo and a line-of-sight halo. Furthermore, it does not take into account degeneracies between an extra subhalo and the uncertain properties of the main lens. More in keeping with analyses of real-world observations, we regard a line-of-sight halo as detectable only if adding it to a smooth model generates a statistically significant improvement in the reconstructed image. We find that the number density of detectable line-of-sight perturbers has been overestimated by as much as a factor of two in the previous literature. For typical lensing geometries and configurations, very deep imaging is sensitive to twice as many line-of-sight perturbers as subhaloes, but moderate depth imaging is sensitive to only slightly more line-of-sight perturbers than subhaloes., Accepted by MNRAS. Comments welcome