Observation and Confirmation of Six Strong-Lensing Systems in the Dark Energy Survey Science Verification Data*

Nord, B.; Buckley-Geer, E.; Lin, H.; Diehl, H. T.; Helsby, Jennifer; Kuropatkin, N.; Amara, Adam; Collett, Thomas E.; Allam, S.; Caminha, G. B.; Bom, C. de; Desai, S.; Dúmet-Montoya, H. S.; Pereira, Maria Elidaiana da Silva; Finley, D. A.; Flaugher, B.; Furlanetto, C.; Gaitsch, Hallie; Gill, M. S.; Merritt, K. W.; More, Anupreeta; Tucker, D. L.; Saro, A.; Rykoff, E. S.; Rozo, Eduardo; Birrer, Simon; Abdalla, F. B.; Agnello, Adriano; Auger, M. W.; Brunner, Róbert; Kind, M. Carrasco; Castander, F. J.; Cunha, C. E.; Costa, L. N. da; Foley, R. J.; Gerdes, D. W.; Glazebrook, Karl; Gschwend, J.; Hartley, W. G.; Keßler, R.; Lagattuta, David J.; Lewis, Geraint F.; Maia, M. A. G.; Makler, M.; Menanteau, F.; Niernberg, A.; Scolnic, Dan; Vieira, J. D.; Gramillano, R.; Abbott, T. M. C.; Banerji, M.; Benoit-Lévy, A.; Burke, D. L.; Capozzi, D.; Rosell, A. Carnero; Carretero, J.; D’Andrea, C. B.; Dietrich, J. P.; Doel, P.; Evrard, A. E.; Frieman, J.; Gaztañaga, E.; Honscheid, K.; James, D. J.; Kuêhn, K.; Li, T. S.; Lima, M.; Marshall, J. L.; Martini, Paul; Miquel, R.; Neilsen, Eric H.; Nichol, R. C.; Ogando, R. L. C.; Plazas, A. A.; Romer, A. K.; Šako, M.; Sánchez, E.; Scarpine, V.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Suchyta, E.; Swanson, M. E. C.; Tarlè, G.; Thaler, J. J.; Walker, A. R.; Wester, W. C.; Zhang, Y.

doi:10.3847/0004-637x/827/1/51

Cited by 27 publications

(22 citation statements)

References 74 publications

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“…Two have both blue and red candidate sources at differing distance from the candidate lens. Using a Gemini Large and Long Program 45 over two years we have spectroscopically confirmed 13 of the systems presented here (Nord et al 2016;B. Nord et al 2017, in preparation;Collett et al 2017;Lin et al 2017), which is 3.5% of the sample.…”

Section: Discussionmentioning

confidence: 73%

“…redMaPPer was used to produce a catalog of galaxy clusters where the richness, defined as the sum of the membership probability of every galaxy in the cluster field (Rozo et al 2009), was greater than 20. The search of 786 such clusters in SVA1 has been previously described (Nord et al 2016). Here, we present Note.Names, algorithms that detected the system, the visual inspection rank, average radius, and references to detections in other papers.…”

Section: Search Around Redmapper Galaxy Clusters and Redmagic Galaxiesmentioning

confidence: 99%

“…The HSC SSP sample is comparable in size and complementary to the result of this paper, as their candidates are principally galaxy-galaxy lenses with a small Einstein radius. Previous searches of the Dark Energy Survey data initially yielded six confirmed strongly lensed galaxies (Nord et al 2016) in the early DES data, and more recently yielded eight more(B. Nord et al 2017, in preparation), and four gravitationally lensed quasars (Agnello et al 2015;Lin et al 2017;Ostrovski et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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The DES Bright Arcs Survey: Hundreds of Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey Science Verification and Year 1 Observations

Diehl¹,

Buckley-Geer²,

Lindgren³

et al. 2017

ApJS

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We report the results of searches for strong gravitational lens systems in the Dark Energy Survey (DES) Science Verification and Year 1 observations. The Science Verification data span approximately 250 sq. deg. with a median i-band limiting magnitude for extended objects (10σ) of 23.0. The Year 1 data span approximately 2000 sq. deg. and have an i-band limiting magnitude for extended objects (10σ) of 22.9. As these data sets are both wide and deep, they are particularly useful for identifying strong gravitational lens candidates. Potential strong gravitational lens candidate systems were initially identified based on a color and magnitude selection in the DES The Astrophysical Journal Supplement Series, 232:15 (28pp), 2017 September https://doi.org/10.3847/1538-4365/aa8667 © 2017. The American Astronomical Society. All rights reserved.1 object catalogs or because the system is at the location of a previously identified galaxy cluster. Cutout images of potential candidates were then visually scanned using an object viewer and numerically ranked according to whether or not we judged them to be likely strong gravitational lens systems. Having scanned nearly 400,000 cutouts, we present 374 candidate strong lens systems, of which 348 are identified for the first time. We provide the R.A. and decl., the magnitudes and photometric properties of the lens and source objects, and the distance (radius) of the source(s) from the lens center for each system.

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Section: Discussionmentioning

confidence: 73%

Section: Search Around Redmapper Galaxy Clusters and Redmagic Galaxiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The DES Bright Arcs Survey: Hundreds of Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey Science Verification and Year 1 Observations

Diehl¹,

Buckley-Geer²,

Lindgren³

et al. 2017

ApJS

Self Cite

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“…Recent efforts have had great success finding the brightest strongly lensed galaxies in wide-area surveys, primarily in the north. The new giant arcs presented here are a preliminary step toward extending the search for highly magnified distant galaxies into the south (e.g., Buckley-Geer et al 2011;Nord et al 2016), and will support future strong lensing analyses of SPT strong lensing clusters, as well as tests of giant arc statistics within the SPT cluster sample.…”

Section: Giant Arc Redshiftsmentioning

confidence: 70%

SPT-Gmos: A Gemini/Gmos-South Spectroscopic Survey of Galaxy Clusters in the SPT-Sz Survey

Bayliss

Ruel

Stubbs

et al. 2016

ApJS

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We present the results of SPT-GMOS, a spectroscopic survey with the Gemini Multi-Object Spectrograph (GMOS) on Gemini South. The targets of SPT-GMOS are galaxy clusters identified in the SPT-SZ survey, a millimeter-wave survey of 2500 deg 2 of the southern sky using the South Pole Telescope (SPT). Multi-object spectroscopic observations of 62 SPT-selected galaxy clusters were performed between 2011 January and 2015 December, yielding spectra with radial velocity measurements for 2595 sources. We identify 2243 of these sources as galaxies, and 352 as stars. Of the galaxies, we identify 1579 as members of SPT-SZ galaxy clusters. The primary goal of these observations was to obtain spectra of cluster member galaxies to estimate cluster redshifts and velocity dispersions. We describe the full spectroscopic data set and resulting data products, including galaxy redshifts, cluster redshifts, and velocity dispersions, and measurements of several well-known spectral indices for each galaxy: the equivalent width, W, of [O II] λλ3727, 3729 and H-δ, and the 4000 Å break strength, D4000. We use the spectral indices to classify galaxies by spectral type (i.e., passive, post-starburst, starforming), and we match the spectra against photometric catalogs to characterize spectroscopically observed cluster members as a function of brightness (relative to m å ). Finally, we report several new measurements of redshifts for ten bright, strongly lensed background galaxies in the cores of eight galaxy clusters. Combining the SPT-GMOS data set with previous spectroscopic follow-up of SPT-SZ galaxy clusters results in spectroscopic measurements for >100 clusters, or ∼20% of the full SPT-SZ sample.

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“…Investigations from the ground include follow-ups of clusters (Luppino et al 1999;Zaritsky & Gonzalez 2003;Hennawi et al 2008;Kausch et al 2010;Furlanetto et al 2013a) and galaxies (Willis et al 2006), and searches in wide-field surveys, such as the Red-Sequence Cluster Survey (RCS; Gladders et al 2003;Bayliss 2012), Sloan Digital Sky Survey (SDSS; Estrada et al 2007;Belokurov et al 2009;Kubo et al 2010;Wen et al 2011;Bayliss 2012), Deep Lens Survey (DLS; Kubo & Dell'Antonio 2008), Canada-France-Hawaii Telescope A&A 597, A135 (2017) (CFHT) Legacy Survey (CFHTLS; Cabanac et al 2007;More et al 2012;Maturi et al 2014;Gavazzi et al 2014;More et al 2016;Paraficz et al 2016), CFHT Stripe 82 Survey (CS82; Caminha, More et al, in prep. ), and Dark Energy Survey 1 (DES; The Dark Energy Survey Collaboration 2005) Science Verification data (Nord et al 2016).…”

Section: Introductionmentioning

confidence: 99%

A neural network gravitational arc finder based on the Mediatrix filamentation method

Makler

Albuquerque

Brandt

2017

A&A

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Context. Automated arc detection methods are needed to scan the ongoing and next-generation wide-field imaging surveys, which are expected to contain thousands of strong lensing systems. Arc finders are also required for a quantitative comparison between predictions and observations of arc abundance. Several algorithms have been proposed to this end, but machine learning methods have remained as a relatively unexplored step in the arc finding process. Aims. In this work we introduce a new arc finder based on pattern recognition, which uses a set of morphological measurements that are derived from the Mediatrix filamentation method as entries to an artificial neural network (ANN). We show a full example of the application of the arc finder, first training and validating the ANN on simulated arcs and then applying the code on four Hubble Space Telescope (HST) images of strong lensing systems. Methods. The simulated arcs use simple prescriptions for the lens and the source, while mimicking HST observational conditions. We also consider a sample of objects from HST images with no arcs in the training of the ANN classification. We use the training and validation process to determine a suitable set of ANN configurations, including the combination of inputs from the Mediatrix method, so as to maximize the completeness while keeping the false positives low. Results. In the simulations the method was able to achieve a completeness of about 90% with respect to the arcs that are input into the ANN after a preselection. However, this completeness drops to ∼70% on the HST images. The false detections are on the order of 3% of the objects detected in these images. Conclusions. The combination of Mediatrix measurements with an ANN is a promising tool for the pattern-recognition phase of arc finding. More realistic simulations and a larger set of real systems are needed for a better training and assessment of the efficiency of the method.

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Observation and Confirmation of Six Strong-Lensing Systems in the Dark Energy Survey Science Verification Data*

Cited by 27 publications

References 74 publications

The DES Bright Arcs Survey: Hundreds of Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey Science Verification and Year 1 Observations

The DES Bright Arcs Survey: Hundreds of Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey Science Verification and Year 1 Observations

SPT-Gmos: A Gemini/Gmos-South Spectroscopic Survey of Galaxy Clusters in the SPT-Sz Survey

A neural network gravitational arc finder based on the Mediatrix filamentation method

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