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Register a new userStrong Lens Models for 37 Clusters of Galaxies from the SDSS Giant Arcs Survey
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We present strong gravitational lensing models for 37 galaxy clusters from the SDSS Giant Arcs Survey. We combine data from multi-band Hubble Space Telescope WFC3imaging, with ground-based imaging and spectroscopy from Magellan, Gemini, APO, and MMT, in order to detect and spectroscopically confirm new multiply-lensed background sources behind the clusters. We report spectroscopic or photometric redshifts of sources in these fields, including cluster galaxies and background sources. Based on all available lensing evidence, we construct and present strong lensing mass models for these galaxy clusters.
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Strong gravitational lensing by galaxy clusters magnifies background galaxies, enhancing our ability to discover statistically significant samples of galaxies at z>6, in order to constrain the high-redshift galaxy luminosity functions. Here, we present the first five lens models out of the Reionization Lensing Cluster Survey (RELICS) Hubble Treasury Program, based on new HST WFC3/IR and ACS imaging of the clusters RXC J0142.9+4438, Abell 2537, Abell 2163, RXC J2211.7-0349, and ACT-CLJ0102-49151. The derived lensing magnification is essential for estimating the intrinsic properties of high-redshift galaxy candidates, and properly accounting for the survey volume. We report on new spectroscopic redshifts of multiply imaged lensed galaxies behind these clusters, which are used as constraints, and detail our strategy to reduce systematic uncertainties due to lack of spectroscopic information. In addition, we quantify the uncertainty on the lensing magnification due to statistical and systematic errors related to the lens modeling process, and find that in all but one cluster, the magnification is constrained to better than 20% in at least 80% of the field of view, including statistical and systematic uncertainties. The five clusters presented in this paper span the range of masses and redshifts of the clusters in the RELICS program. We find that they exhibit similar strong lensing efficiencies to the clusters targeted by the Hubble Frontier Fields within the WFC3/IR field of view. Outputs of the lens models are made available to the community through the Mikulski Archive for Space Telescopes
We use nine different galaxy formation scenarios in ten cosmological simulation boxes from the EAGLE suite of {Lambda}CDM hydrodynamical simulations to assess the impact of feedback mechanisms in galaxy formation and compare these to observed strong gravitational lenses. To compare observations with simulations, we create strong lenses with $M_star$ > $10^{11}$ $M_odot$ with the appropriate resolution and noise level, and model them with an elliptical power-law mass model to constrain their total mass density slope. We also obtain the mass-size relation of the simulated lens-galaxy sample. We find significant variation in the total mass density slope at the Einstein radius and in the projected stellar mass-size relation, mainly due to different implementations of stellar and AGN feedback. We find that for lens selected galaxies, models with either too weak or too strong stellar and/or AGN feedback fail to explain the distribution of observed mass-density slopes, with the counter-intuitive trend that increasing the feedback steepens the mass density slope around the Einstein radius ($approx$ 3-10 kpc). Models in which stellar feedback becomes inefficient at high gas densities, or weaker AGN feedback with a higher duty cycle, produce strong lenses with total mass density slopes close to isothermal (i.e. -d log({rho})/d log(r) $approx$ 2.0) and slope distributions statistically agreeing with observed strong lens galaxies in SLACS and BELLS. Agreement is only slightly worse with the more heterogeneous SL2S lens galaxy sample. Observations of strong-lens selected galaxies thus appear to favor models with relatively weak feedback in massive galaxies.
We have carried out a systematic search for galaxy-scale strong lenses in multiband imaging from the Hyper Suprime-Cam (HSC) survey. Our automated pipeline, based on realistic strong-lens simulations, deep neural network classification, and visual inspection, is aimed at efficiently selecting systems with wide image separations (Einstein radii ~1.0-3.0), intermediate redshift lenses (z ~ 0.4-0.7), and bright arcs for galaxy evolution and cosmology. We classified gri images of all 62.5 million galaxies in HSC Wide with i-band Kron radius >0.8 to avoid strict pre-selections and to prepare for the upcoming era of deep, wide-scale imaging surveys with Euclid and Rubin Observatory. We obtained 206 newly-discovered candidates classified as definite or probable lenses with either spatially-resolved multiple images or extended, distorted arcs. In addition, we found 88 high-quality candidates that were assigned lower confidence in previous HSC searches, and we recovered 173 known systems in the literature. These results demonstrate that, aided by limited human input, deep learning pipelines with false positive rates as low as ~0.01% can be very powerful tools for identifying the rare strong lenses from large catalogs, and can also largely extend the samples found by traditional algorithms. We provide a ranked list of candidates for future spectroscopic confirmation.
We report the discovery of 13 confirmed two-image quasar lenses from a systematic search for gravitationally lensed quasars in the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS). We adopted a methodology similar to that used in the SDSS Quasar Lens Search (SQLS). In addition to the confirmed lenses, we report 11 quasar pairs with small angular separations ($lesssim$2) confirmed from our spectroscopy, which are either projected pairs, physical binaries, or possibly quasar lens systems whose lens galaxies have not yet been detected. The newly discovered quasar lens system, SDSS J1452+4224 at zs$approx$4.8 is one of the highest redshift multiply imaged quasars found to date. Furthermore, we have over 50 good lens candidates yet to be followed up. Owing to the heterogeneous selection of BOSS quasars, the lens sample presented here does not have a well-defined selection function.
We performed a search for strong lens galaxy-scale systems in the first data release of the Dark Energy Survey (DES), from a color-selected parent sample of 18~745~029 Luminous Red Galaxies (LRGs). Our search was based on a Convolutional Neural Network (CNN) to grade our LRG selection with values between 0 (non-lens) and 1 (lens). Our training set was data-driven, i.e. using lensed sources taken from HST COSMOS images and where the light distribution of the lens plane was taken directly from DES images of our LRGs. A total of 76~582 cutouts obtained a score above 0.9. These were visually inspected and resulted in two catalogs. The first one contains 405 lens candidates, where 90 present clear lensing features and counterparts, while the others 315 require more evidence, such as higher resolution images or spectra to be conclusive. A total of 186 candidates were totally new identified in this search. The second catalog includes 539 ring galaxy candidates that will be useful to train CNNs against this type of false positives. For the 90 best lens candidates we carried out color-based deblending of the lens and source light without fitting any analytical profile to the data. The method turned out to be very efficient in the deblending, even for very compact objects and for objects with very complex morphology. Finally, from the 90 best lens candidates we selected 52 systems having one single deflector, to test an automated modeling pipeline which successfully modeled 79% of the sample within an acceptable amount of computing time.
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