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Register a new userStrong Gravitational Lens Candidates in the GOODS ACS Fields
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Authors
C. D. Fassnacht
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We present results from a systematic search for strong gravitational lenses in the GOODS ACS data. The search technique involves creating a sample of likely lensing galaxies, which we define as massive early-type galaxies in a redshift range 0.3 < z <1.3. The target galaxies are selected by color and magnitude, giving a sample of 1092 galaxies. For each galaxy in the sample, we subtract a smooth description of the galaxy light from the z_{850}-band data. The residuals are examined, along with true-color images created from the B_{435}V_{606}i_{775} data, for morphologies indicative of strong lensing. We present our six most promising lens candidates, as well as our full list of candidates.
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We present the definitive data for the full sample of 131 strong gravitational lens candidates observed with the Advanced Camera for Surveys (ACS) aboard the Hubble Space Telescope by the Sloan Lens ACS (SLACS) Survey. All targets were selected for higher-redshift emission lines and lower-redshift continuum in a single Sloan Digital Sky Survey (SDSS) spectrum. The foreground galaxies are primarily of early-type morphology, with redshifts from approximately 0.05 to 0.5 and velocity dispersions from 160 km/s to 400 km/s; the faint background emission-line galaxies have redshifts ranging from about 0.2 to 1.2. We confirm 70 systems showing clear evidence of multiple imaging of the background galaxy by the foreground galaxy, as well as an additional 19 systems with probable multiple imaging. For 63 clear lensing systems, we present singular isothermal ellipsoid and light-traces-mass gravitational lens models fitted to the ACS imaging data. These strong-lensing mass measurements are supplemented by magnitudes and effective radii measured from ACS surface-brightness photometry and redshifts and velocity dispersions measured from SDSS spectroscopy. These data constitute a unique resource for the quantitative study of the inter-relations between mass, light, and kinematics in massive early-type galaxies. We show that the SLACS lens sample is statistically consistent with being drawn at random from a parent sample of SDSS galaxies with comparable spectroscopic parameters and effective radii, suggesting that the results of SLACS analyses can be generalized to the massive early-type population.
We report ten lens candidates in the E-CDFS from the GEMS survey. Nine of the systems are new detections and only one of the candidates is a known lens system. For the most promising five systems including the known lens system, we present results from preliminary lens mass modelling, which tests if the candidates are plausible lens systems. Photometric redshifts of the candidate lens galaxies are obtained from the COMBO-17 galaxy catalog. Stellar masses of the candidate lens galaxies within the Einstein radius are obtained by using the $z$-band luminosity and the $V-z$ color-based stellar mass-to-light ratios. As expected, the lensing masses are found to be larger than the stellar masses of the candidate lens galaxies. These candidates have similar dark matter fractions as compared to lenses in SLACS and COSMOS. They also roughly follow the halo mass-stellar mass relation predicted by the subhalo abundance matching technique. One of the candidate lens galaxies qualifies as a LIRG and may not be a true lens because the arc-like feature in the system is likely to be an active region of star formation in the candidate lens galaxy. Amongst the five best candidates, one is a confirmed lens system, one is a likely lens system, two are less likely to be lenses and the status of one of the candidates is ambiguous. Spectroscopic follow-up of these systems is still required to confirm lensing and/or for more accurate determination of the lens masses and mass density profiles.
Large scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100,000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. (abridged)
Strong gravitationally lensed quasars provide powerful means to study galaxy evolution and cosmology. Current and upcoming imaging surveys will contain thousands of new lensed quasars, augmenting the existing sample by at least two orders of magnitude. To find such lens systems, we built a robot, CHITAH, that hunts for lensed quasars by modeling the configuration of the multiple quasar images. Specifically, given an image of an object that might be a lensed quasar, CHITAH first disentangles the light from the supposed lens galaxy and the light from the multiple quasar images based on color information. A simple rule is designed to categorize the given object as a potential four-image (quad) or two-image (double) lensed quasar system. The configuration of the identified quasar images is subsequently modeled to classify whether the object is a lensed quasar system. We test the performance of CHITAH using simulated lens systems based on the Canada-France-Hawaii Telescope Legacy Survey. For bright quads with large image separations (with Einstein radius $r_{rm ein}>1.1$) simulated using Gaussian point-spread functions, a high true-positive rate (TPR) of $sim$90% and a low false-positive rate of $sim$$3%$ show that this is a promising approach to search for new lens systems. We obtain high TPR for lens systems with $r_{rm ein}gtrsim0.5$, so the performance of CHITAH is set by the seeing. We further feed a known gravitational lens system, COSMOS 5921$+$0638, to CHITAH, and demonstrate that CHITAH is able to classify this real gravitational lens system successfully. Our newly built CHITAH is omnivorous and can hunt in any ground-based imaging surveys.
We report the results of $EasyCritics$, a fully automated algorithm for the efficient search of strong-lensing (SL) regions in wide-field surveys, applied to the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). By using only the photometric information of the brightest elliptical galaxies distributed over a wide redshift range ($smash{0.2 lesssim z lesssim 0.9}$) and without requiring the identification of arcs, our algorithm produces lensing potential models and catalogs of critical curves of the entire survey area. We explore several parameter set configurations in order to test the efficiency of our approach. In a specific configuration, $EasyCritics$ generates only $sim1200$ possibly super-critical regions in the CFHTLS area, drastically reducing the effective area for inspection from $154$ sq. deg to $sim0.623$ sq. deg, $i.e.$ by more than two orders of magnitude. Among the pre-selected SL regions, we identify 32 of the 44 previously known lenses on the group and cluster scale, and discover 9 new promising lens candidates. The detection rate can be easily improved to $sim82%$ by a simple modification in the parameter set, but at the expense of increasing the total number of possible SL candidates. Note that $EasyCritics$ is fully complementary to other arc-finders since we characterize lenses instead of directly identifying arcs. Although future comparisons against numerical simulations are required for fully assessing the efficiency of $EasyCritics$, the algorithm seems very promising for upcoming surveys covering $smash{10^{4}}$ sq. deg, such as the $Euclid$ mission and $LSST$, where the pre-selection of candidates for any kind of SL analysis will be indispensable due to the expected enormous data volume.
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