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Register a new userA Spectroscopic Survey of the Fields of 28 Strong Gravitational Lenses: Implications for $H_0$
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Strong gravitational lensing provides an independent measurement of the Hubble parameter ($H_0$). One remaining systematic is a bias from the additional mass due to a galaxy group at the lens redshift or along the sightline. We quantify this bias for more than 20 strong lenses that have well-sampled sightline mass distributions, focusing on the convergence $kappa$ and shear $gamma$. In 23% of these fields, a lens group contributes a $ge$1% convergence bias; in 57%, there is a similarly significant line-of-sight group. For the nine time delay lens systems, $H_0$ is overestimated by 11$^{+3}_{-2}$% on average when groups are ignored. In 67% of fields with total $kappa ge$ 0.01, line-of-sight groups contribute $gtrsim 2times$ more convergence than do lens groups, indicating that the lens group is not the only important mass. Lens environment affects the ratio of four (quad) to two (double) image systems; all seven quads have lens groups while only three of 10 doubles do, and the highest convergences due to lens groups are in quads. We calibrate the $gamma$-$kappa$ relation: $log(kappa_{rm{tot}}) = (1.94 pm 0.34) log(gamma_{rm{tot}}) + (1.31 pm 0.49)$ with a rms scatter of 0.34 dex. Shear, which, unlike convergence, can be measured directly from lensed images, can be a poor predictor of $kappa$; for 19% of our fields, $kappa$ is $gtrsim 2gamma$. Thus, accurate cosmology using strong gravitational lenses requires precise measurement and correction for all significant structures in each lens field.
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With a large, unique spectroscopic survey in the fields of 28 galaxy-scale strong gravitational lenses, we identify groups of galaxies in the 26 adequately-sampled fields. Using a group finding algorithm, we find 210 groups with at least five member galaxies; the median number of members is eight. Our sample spans redshifts of 0.04 $le z_{grp} le$ 0.76 with a median of 0.31, including 174 groups with $0.1 < z_{grp} < 0.6$. Groups have radial velocity dispersions of 60 $le sigma_{grp} le$ 1200 km s$^{-1}$ with a median of 350 km s$^{-1}$. We also discover a supergroup in field B0712+472 at $z =$ 0.29 consisting of three main groups. We recover groups similar to $sim$ 85% of those previously reported in these fields within our redshift range of sensitivity and find 187 new groups with at least five members. The properties of our group catalog, specifically 1) the distribution of $sigma_{grp}$, 2) the fraction of all sample galaxies that are group members, and 3) the fraction of groups with significant substructure, are consistent with those for other catalogs. The distribution of group virial masses agrees well with theoretical expectations. Of the lens galaxies, 12 of 26 (46%) (B1422+231, B1600+434, B2114+022, FBQS J0951+2635, HE0435-1223, HST J14113+5211, MG0751+2716, MGJ1654+1346, PG 1115+080, Q ER 0047-2808, RXJ1131-1231, and WFI J2033-4723) are members of groups with at least five galaxies, and one more (B0712+472) belongs to an additional, visually identified group candidate. There are groups not associated with the lens that still are likely to affect the lens model; in six of 25 (24%) fields (excluding the supergroup), there is at least one massive ($sigma_{grp} ge$ 500 km s$^{-1}$) group or group candidate projected within 2$^{prime}$ of the lens.
We perform a semi-automated search for strong gravitational lensing systems in the 9,000 deg$^2$ Dark Energy Camera Legacy Survey (DECaLS), part of the DESI Legacy Imaging Surveys (Dey et al.). The combination of the depth and breadth of these surveys are unparalleled at this time, making them particularly suitable for discovering new strong gravitational lensing systems. We adopt the deep residual neural network architecture (He et al.) developed by Lanusse et al. for the purpose of finding strong lenses in photometric surveys. We compile a training set that consists of known lensing systems in the Legacy Surveys and DES as well as non-lenses in the footprint of DECaLS. In this paper we show the results of applying our trained neural network to the cutout images centered on galaxies typed as ellipticals (Lang et al.) in DECaLS. The images that receive the highest scores (probabilities) are visually inspected and ranked. Here we present 335 candidate strong lensing systems, identified for the first time.
We present a spectroscopic survey for strong galaxy-galaxy lenses. Exploiting optimal sight-lines to massive, bulge-dominated galaxies at redshifts $z sim 0.4$ with wide-field, multifibre spectroscopy, we anticipate the detection of 10-20 lensed Lyman-$alpha$ emitting galaxies at redshifts $z simgreat 3$ from a sample of 2000 deflectors. Initial spectroscopic observations are described and the prospects for constraining the emission-line luminosity function of the Lyman-$alpha$ emitting population are outlined.
We present a sample of 16 likely strong gravitational lenses identified in the VST Optical Imaging of the CDFS and ES1 fields (VOICE survey) using Convolutional Neural Networks (CNNs). We train two different CNNs on composite images produced by superimposing simulated gravitational arcs on real Luminous Red Galaxies observed in VOICE. Specifically, the first CNN is trained on single-band images and more easily identifies systems with large Einstein radii, while the second one, trained on composite RGB images, is more accurate in retrieving systems with smaller Einstein radii. We apply both networks to real data from the VOICE survey, taking advantage of the high limiting magnitude (26.1 in the r-band) and low PSF FWHM (0.8 in the r-band) of this deep survey. We analyse $sim21,200$ images with $mag_r<21.5$, identifying 257 lens candidates. To retrieve a high-confidence sample and to assess the accuracy of our technique, nine of the authors perform a visual inspection. Roughly 75% of the systems are classified as likely lenses by at least one of the authors. Finally, we assemble the LIVE sample (Lenses In VoicE) composed by the 16 systems passing the chosen grading threshold. Three of these candidates show likely lensing features when observed by the Hubble Space Telescope. This work represents a further confirmation of the ability of CNNs to inspect large samples of galaxies searching for gravitational lenses. These algorithms will be crucial to exploit the full scientific potential of forthcoming surveys with the Euclid satellite and the Vera Rubin Observatory
The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyze sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the detectable lensed sources is only a very small fraction of the full source population. We apply for the first time a morphological classification method based on a Convolutional Neural Network (CNN) for recognizing strong gravitational lenses in $255$ square degrees of the Kilo Degree Survey (KiDS), one of the current-generation optical wide surveys. The CNN is currently optimized to recognize lenses with Einstein radii $gtrsim 1.4$ arcsec, about twice the $r$-band seeing in KiDS. In a sample of $21789$ colour-magnitude selected Luminous Red Galaxies (LRG), of which three are known lenses, the CNN retrieves 761 strong-lens candidates and correctly classifies two out of three of the known lenses. The misclassified lens has an Einstein radius below the range on which the algorithm is trained. We down-select the most reliable 56 candidates by a joint visual inspection. This final sample is presented and discussed. A conservative estimate based on our results shows that with our proposed method it should be possible to find $sim100$ massive LRG-galaxy lenses at $zlsim 0.4$ in KiDS when completed. In the most optimistic scenario this number can grow considerably (to maximally $sim$2400 lenses), when widening the colour-magnitude selection and training the CNN to recognize smaller image-separation lens systems.
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