The inner dark matter distribution of the Cosmic Horseshoe (J1148+1930) with gravitational lensing and dynamics

We present a detailed analysis of the inner mass structure of the Cosmic Horseshoe (J1148+1930) strong gravitational lens system observed with the Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3). In addition to the spectacular Einstein ring, this systems shows a radial arc. We obtained the redshift of the radial arc counter image $z_\text{s,r} = 1.961 \pm 0.001$ from Gemini observations. To disentangle the dark and luminous matter, we consider three different profiles for the dark matter distribution: a power-law profile, the NFW, and a generalized version of the NFW profile. For the luminous matter distribution, we base it on the observed light distribution that is fitted with three components: a point mass for the central light component resembling an active galactic nucleus, and the remaining two extended light components scaled by a constant M/L. To constrain the model further, we include published velocity dispersion measurements of the lens galaxy and perform a self-consistent lensing and axisymmetric Jeans dynamical modeling. Our model fits well to the observations including the radial arc, independent of the dark matter profile. Depending on the dark matter profile, we get a dark matter fraction between 60 % and 70 %. With our composite mass model we find that the radial arc helps to constrain the inner dark matter distribution of the Cosmic Hoseshoe independently of the dark matter profile.Comment: 19 pages, 14 figures, 8 tables, submitted to A&

Cosmography from two-image lens systems: overcoming the lens profile slope degeneracy

The time delays between the multiple images of a strong lens system, together with a model of the lens mass distribution, allow a one-step measurement of a cosmological distance, namely, the "time-delay distance" of the lens (D_dt) that encodes cosmological information. The time-delay distance depends sensitively on the radial profile slope of the lens mass distribution; consequently, the lens slope must be accurately constrained for cosmological studies. We show that the slope cannot be constrained in two-image systems with single-component compact sources, whereas it can be constrained in systems with two-component sources provided the separation between the image components can be measured with milliarcsecond precisions, which is not feasible in most systems. In contrast, we demonstrate that spatially extended images of the source galaxy in two-image systems break the radial slope degeneracy and allow D_dt to be measured with uncertainties of a few percent. Deep and high-resolution imaging of the lens systems are needed to reveal the extended arcs, and stable point spread functions are required for our lens modelling technique. Two-image systems, no longer plagued by the radial profile slope degeneracy, would augment the sample of useful time-delay lenses by a factor of ~6, providing substantial advances for cosmological studies.Comment: 14 pages, 9 figures, revisions based on referee's comments, accepted for publication in MNRA

GLaD: Gravitational Lensing and Dynamics, combined analysis to unveil properties of high-redshift galaxies

Dynamical modelling of Integral-Field-Unit (IFU) stellar kinematics is a powerful tool to unveil the dynamical structure and mass build-up of galaxies in the local Universe, while gravitational lensing is nature's cosmic telescope to explore the properties of galaxies beyond the local Universe. We present a new approach which unifies dynamical modelling of galaxies with the magnification power of strong gravitational lensing, to reconstruct the structural and dynamical properties of high-redshift galaxies. By means of axisymmetric Jeans modelling, we create a dynamical model of the source galaxy, assuming a surface brightness and surface mass density profile. We then predict how the source's surface brightness and kinematics would look like when lensed by the foreground mass distribution and compare with the mock observed arcs of strong gravitational lensing systems. For demonstration purposes, we create and analyse mock data of the strong lensing system RX J1131-1231. By modelling both the lens and source, we recover the dynamical mass within the effective radius of strongly lensed high-redshift sources within 5% uncertainty, and we improve the constraints on the lens mass parameters by up to 50%. This machinery is particularly well suited for future observations from large segmented-mirror telescopes, such as the James Webb Space Telescope, that will yield high sensitivity and angular-resolution IFU data for studying distant and faint galaxies.Comment: 16 pages, 13 figure

The Hubble Constant determined through an inverse distance ladder including quasar time delays and Type Ia supernovae

Context. The precise determination of the present-day expansion rate of the Universe, expressed through the Hubble constant $H_0$, is one of the most pressing challenges in modern cosmology. Assuming flat $\Lambda$CDM, $H_0$ inference at high redshift using cosmic-microwave-background data from Planck disagrees at the 4.4$\sigma$ level with measurements based on the local distance ladder made up of parallaxes, Cepheids and Type Ia supernovae (SNe Ia), often referred to as "Hubble tension". Independent, cosmological-model-insensitive ways to infer $H_0$ are of critical importance. Aims. We apply an inverse-distance-ladder approach, combining strong-lensing time-delay-distance measurements with SN Ia data. By themselves, SNe Ia are merely good relative distance indicators, but by anchoring them to strong gravitational lenses one can obtain an $H_0$ measurement that is relatively insensitive to other cosmological parameters. Methods. A cosmological parameter estimate is performed for different cosmological background models, both for strong-lensing data alone and for the combined lensing + SNe Ia data sets. Results. The cosmological-model dependence of strong-lensing $H_0$ measurements is significantly mitigated through the inverse distance ladder. In combination with SN Ia data, the inferred $H_0$ consistently lies around 73-74 km s$^{-1}$ Mpc$^{-1}$, regardless of the assumed cosmological background model. Our results agree nicely with those from the local distance ladder, but there is a >2$\sigma$ tension with Planck results, and a ~1.5$\sigma$ discrepancy with results from an inverse distance ladder including Planck, Baryon Acoustic Oscillations and SNe Ia. Future strong-lensing distance measurements will reduce the uncertainties in $H_0$ from our inverse distance ladder.Comment: 5 pages, 3 figures, A&A letters accepted versio

HOLISMOKES -- X. Comparison between neural network and semi-automated traditional modeling of strong lenses

Modeling of strongly gravitationally lensed galaxies is often required in order to use them as astrophysical or cosmological probes. With current and upcoming wide-field imaging surveys, the number of detected lenses is increasing significantly such that automated and fast modeling procedures for ground-based data are urgently needed. This is especially pertinent to short-lived lensed transients in order to plan follow-up observations. Therefore, we present in a companion paper (submitted) a neural network predicting the parameter values with corresponding uncertainties of a Singular Isothermal Ellipsoid (SIE) mass profile with external shear. In this work, we present a newly-developed pipeline glee_auto.py to model consistently any galaxy-scale lensing system. In contrast to previous automated modeling pipelines that require high-resolution images, glee_auto.py is optimized for ground-based images such as those from the Hyper-Suprime-Cam (HSC) or the upcoming Rubin Observatory Legacy Survey of Space and Time. We further present glee_tools.py, a flexible automation code for individual modeling that has no direct decisions and assumptions implemented. Both pipelines, in addition to our modeling network, minimize the user input time drastically and thus are important for future modeling efforts. We apply the network to 31 real galaxy-scale lenses of HSC and compare the results to the traditional models. In the direct comparison, we find a very good match for the Einstein radius especially for systems with $\theta_E \gtrsim 2$". The lens mass center and ellipticity show reasonable agreement. The main discrepancies are on the external shear as expected from our tests on mock systems. In general, our study demonstrates that neural networks are a viable and ultra fast approach for measuring the lens-galaxy masses from ground-based data in the upcoming era with $\sim10^5$ lenses expected.Comment: 17+28 pages, 7+31 figures, 2+5 tables, submitted to A&

Strongly lensed SNe Ia in the era of LSST: observing cadence for lens discoveries and time-delay measurements

The upcoming Large Synoptic Survey Telescope (LSST) will detect many strongly lensed Type Ia supernovae (LSNe Ia) for time-delay cosmography. This will provide an independent and direct way for measuring the Hubble constant $H_0$, which is necessary to address the current $4.4 \sigma$ tension in $H_0$ between the local distance ladder and the early Universe measurements. We present a detailed analysis of different observing strategies for the LSST, and quantify their impact on time-delay measurement between multiple images of LSNe Ia. For this, we produced microlensed mock-LSST light curves for which we estimated the time delay between different images. We find that using only LSST data for time-delay cosmography is not ideal. Instead, we advocate using LSST as a discovery machine for LSNe Ia, enabling time delay measurements from follow-up observations from other instruments in order to increase the number of systems by a factor of 2 to 16 depending on the observing strategy. Furthermore, we find that LSST observing strategies, which provide a good sampling frequency (the mean inter-night gap is around two days) and high cumulative season length (ten seasons with a season length of around 170 days per season), are favored. Rolling cadences subdivide the survey and focus on different parts in different years; these observing strategies trade the number of seasons for better sampling frequency. In our investigation, this leads to half the number of systems in comparison to the best observing strategy. Therefore rolling cadences are disfavored because the gain from the increased sampling frequency cannot compensate for the shortened cumulative season length. We anticipate that the sample of lensed SNe Ia from our preferred LSST cadence strategies with rapid follow-up observations would yield an independent percent-level constraint on $H_0$.Comment: 25 pages, 22 figures; accepted for publication in A&

The Halos of Satellite Galaxies: the Companion of the Massive Elliptical Lens SL2S J08544-0121

Strong gravitational lensing by groups or clusters of galaxies provides a powerful technique to measure the dark matter properties of individual lens galaxies. We study in detail the mass distribution of the satellite lens galaxy in the group-scale lens SL2S J08544-0121 by modelling simultaneously the spatially extended surface brightness distribution of the source galaxy and the lens mass distribution using Markov chain Monte Carlo methods. In particular, we measure the dark matter halo size of the satellite lens galaxy to be 6.0^{+2.9}_{-2.0} kpc with a fiducial velocity dispersion of 127^{+21}_{-12} km/s. This is the first time the size of an individual galaxy halo in a galaxy group has been measured using strong gravitational lensing without assumptions of mass following light. We verify the robustness of our halo size measurement using mock data resembling our lens system. Our measurement of the halo size is compatible with the estimated tidal radius of the satellite galaxy, suggesting that halos of galaxies in groups experience significant tidal stripping, a process that has been previously observed on galaxies in clusters. Our mass model of the satellite galaxy is elliptical with its major axis misaligned with that of the light by ~50 deg. The major axis of the total matter distribution is oriented more towards the centre of the host halo, exhibiting the radial alignment found in N-body simulations and observational studies of satellite galaxies. This misalignment between mass and light poses a significant challenge to modified Newtonian dynamics.Comment: 13 pages, 10 figures, minor revisions based on referee's comments, accepted for publication in A&

HOLISMOKES -- IV. Efficient mass modeling of strong lenses through deep learning

Modelling the mass distributions of strong gravitational lenses is often necessary to use them as astrophysical and cosmological probes. With the high number of lens systems ($>10^5$) expected from upcoming surveys, it is timely to explore efficient modeling approaches beyond traditional MCMC techniques that are time consuming. We train a CNN on images of galaxy-scale lenses to predict the parameters of the SIE mass model ($x,y,e_x,e_y$, and $\theta_E$). To train the network, we simulate images based on real observations from the HSC Survey for the lens galaxies and from the HUDF as lensed galaxies. We tested different network architectures, the effect of different data sets, and using different input distributions of $\theta_E$. We find that the CNN performs well and obtain with the network trained with a uniform distribution of $\theta_E$ $>0.5"$ the following median values with $1\sigma$ scatter: $\Delta x=(0.00^{+0.30}_{-0.30})"$, $\Delta y=(0.00^{+0.30}_{-0.29})"$, $\Delta \theta_E=(0.07^{+0.29}_{-0.12})"$, $\Delta e_x = -0.01^{+0.08}_{-0.09}$ and $\Delta e_y = 0.00^{+0.08}_{-0.09}$. The bias in $\theta_E$ is driven by systems with small $\theta_E$. Therefore, when we further predict the multiple lensed image positions and time delays based on the network output, we apply the network to the sample limited to $\theta_E>0.8"$. In this case, the offset between the predicted and input lensed image positions is $(0.00_{-0.29}^{+0.29})"$ and $(0.00_{-0.31}^{+0.32})"$ for $x$ and $y$, respectively. For the fractional difference between the predicted and true time delay, we obtain $0.04_{-0.05}^{+0.27}$. Our CNN is able to predict the SIE parameters in fractions of a second on a single CPU and with the output we can predict the image positions and time delays in an automated way, such that we are able to process efficiently the huge amount of expected lens detections in the near future.Comment: 17 pages, 14 Figure

HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images

Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology. Especially with the large number of detections in current and upcoming surveys such as the Rubin Legacy Survey of Space and Time (LSST), it is timely to investigate in automated and fast analysis techniques beyond the traditional and time consuming Markov chain Monte Carlo sampling methods. Building upon our convolutional neural network (CNN) presented in Schuldt et al. (2021b), we present here another CNN, specifically a residual neural network (ResNet), that predicts the five mass parameters of a Singular Isothermal Ellipsoid (SIE) profile (lens center $x$ and $y$, ellipticity $e_x$ and $e_y$, Einstein radius $\theta_E$) and the external shear ($\gamma_{ext,1}$, $\gamma_{ext,2}$) from ground-based imaging data. In contrast to our CNN, this ResNet further predicts a 1$\sigma$ uncertainty for each parameter. To train our network, we use our improved pipeline from Schuldt et al. (2021b) to simulate lens images using real images of galaxies from the Hyper Suprime-Cam Survey (HSC) and from the Hubble Ultra Deep Field as lens galaxies and background sources, respectively. We find overall very good recoveries for the SIE parameters, while differences remain in predicting the external shear. From our tests, most likely the low image resolution is the limiting factor for predicting the external shear. Given the run time of milli-seconds per system, our network is perfectly suited to predict the next appearing image and time delays of lensed transients in time. Therefore, we also present the performance of the network on these quantities in comparison to our simulations. Our ResNet is able to predict the SIE and shear parameter values in fractions of a second on a single CPU such that we are able to process efficiently the huge amount of expected galaxy-scale lenses in the near future.Comment: 16 pages, including 11 figures, accepted for publication by A&