Combining Dark Energy Survey Science Verification data with near-infrared data from the ESO VISTA Hemisphere Survey

Banerji, M.; Jouvel, S.; Lin, Huan; McMahon, R. G.; Lahav, O.; Castander, F. J.; Abdalla, F. B.; Bertin, E.; Bosman, Sarah E. I.; Carnero, A.; Kind, M. Carrasco; Costa, L. N. da; Gerdes, D. W.; Gschwend, J.; Lima, M.; Maia, M. A. G.; Merson, Alex; Miller, C. J.; Ogando, R. L. C.; Pellegrini, P. S.; Reed, S. L.; Saglia, R.; Sánchez, C.; Allam, S.; Annis, J.; Bernstein, G. M.; Bernstein, Joseph P.; Bernstein, R. A.; Capozzi, D.; Childress, M.; Cunha, C. E.; Davis, Tamara M.; DePoy, D.; Desai, S.; Diehl, H. T.; Doel, P.; Findlay, Joseph R.; Finley, D. A.; Flaugher, B.; Frieman, Joshua A.; Gaztañaga, E.; Glazebrook, Karl; González-Fernández, C.; González-Solares, E.; Honscheid, K.; Irwin, M. J.; Jarvis, Matt J.; Kim, A.; Koposov, S. E.; Kuêhn, K.; Kupcu-Yoldas, A.; Lagattuta, David J.; Lewis, James R.; Lidman, C.; Makler, M.; Marriner, J.; Marshall, J. L.; Miquel, R.; Mohr, J. J.; Neilsen, Eric H.; Peoples, J.; Šako, M.; Sánchez, E.; Scarpine, V.; Schindler, R. H.; Schubnell, M.; Sevilla-Noarbe, I.; Sharp, Robert; Soares-Santos, M.; Swanson, M. E. C.; Tarlè, G.; Thaler, J. J.; Tucker, D. L.; Uddin, S. A.; Wechsler, Risa H.; Wester, W. C.; Yuan, F.; Zuntz, Joe

doi:10.1093/mnras/stu2261

Cited by 32 publications

(37 citation statements)

References 55 publications

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“…The detailed survey description and results from the first year are presented in Yuan et al (2015). Briefly, the science objectives of OzDES include obtaining supernova (SN) host-galaxy redshifts for cosmology (e.g., Bazin et al 2011;Campbell et al 2013), spectroscopically classifying active transients, monitoring a sample of active galactic nuclei (AGN) for reverberation mapping (RM -see e.g., Bentz et al 2009;King et al 2015) and potentially for cosmology (Watson et al 2011;King et al 2014), securing redshifts for a wide variety of galaxies to be used for photometric redshift training (e.g., Sánchez et al 2014;Bonnett et al 2016), including a large sample of luminous red galaxies (LRGs; Banerji et al 2015), and using redshifts of selected galaxies to confirm their membership in clusters (e.g., Rozo et al 2016;Rykoff et al 2016). OzDES has already produced several discoveries, including spectroscopy of hundreds of active transients, many new QSOs (Tie et al 2017), and the first FeLoBAL QSO in a post-starburst galaxy (Mudd et al 2016).…”

Section: Introductionmentioning

confidence: 99%

OzDES multifibre spectroscopy for the Dark Energy Survey: 3-yr results and first data release

Childress

Lidman

Davis

et al. 2017

Monthly Notices of the Royal Astronomical Society

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Section: Introductionmentioning

confidence: 99%

OzDES multifibre spectroscopy for the Dark Energy Survey: 3-yr results and first data release

Childress

Lidman

Davis

et al. 2017

Monthly Notices of the Royal Astronomical Society

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“…Similarly to Section 5.2, the g − i limit, which is more relaxed here, is used to minimize contamination from compact galaxies but also excludes highredshift quasars (z>3.5). The median K-band depth in the P15-S1S2 region is also shallower than the median VHS depth over the wider DES-VHS area quoted in Banerji et al (2015). This color selection identifies 269 (93) of the 291 (104) P15-S1S2 quasars with the data necessary to apply this cut, for a completeness of 92% (89%).…”

Section: Gik Selectionmentioning

confidence: 61%

“…Of these 563 (105) quasars, 405 (103) have WISE W1 detections, 308 (101) have WISE W1 and W2 detections, and 291 (104) have VHS K-band detections. Note that the median K-band depth in the P15-S1S2 region is shallower than the median VHS depth over the wider area quoted in Banerji et al (2015).…”

Section: Wisementioning

confidence: 72%

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A Study of Quasar Selection in the Supernova Fields of the Dark Energy Survey

Tie¹,

Martini²,

Mudd³

et al. 2017

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We present a study of quasar selection using the supernova fields of the Dark Energy Survey (DES). We used a quasar catalog from an overlapping portion of the SDSS Stripe 82 region to quantify the completeness and efficiency of selection methods involving color, probabilistic modeling, variability, and combinations of color/ probabilistic modeling with variability. In all cases, we considered only objects that appear as point sources in the DES images. We examine color selection methods based on the Wide-field Infrared Survey Explorer2 color, a mixture of WISE and DES colors (g − i andiW 1), and a mixture of Vista Hemisphere Survey and DES colors (g − i and i − K ). For probabilistic quasar selection, we used XDQSO, an algorithm that employs an empirical multi-wavelength flux model of quasars to assign quasar probabilities. Our variability selection uses the multi-band χ 2 -probability that sources are constant in the DES Year 1 griz-band light curves. The completeness and efficiency are calculated relative to an underlying sample of point sources that are detected in the required selection bands and pass our data quality and photometric error cuts. We conduct our analyses at two magnitude limits, i<19.8 mag and i<22 mag. For the subset of sources with W1 and W2 detections, the -W W 1 2 color or XDQSOz method combined with variability gives the highest completenesses of >85% for both i-band magnitude limits and efficiencies of >80% to the bright limit and >60% to the faint limit; however, the giW1 and giW1+variability methods give the highest quasar surface densities. The XDQSOz method and combinations of W1W2/giW1/XDQSOz with variability are among the better selection methods when both high completeness and high efficiency are desired. We also present the OzDES Quasar Catalog of 1263 spectroscopically confirmed quasars from three years of OzDES observation in the 30 deg 2 of the DES supernova fields. The catalog includes quasars with redshifts up to z∼4 and brighter than i = 22 mag, although the catalog is not complete up to this magnitude limit.

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“…Scientific results from Bonnett et al (2016); Rozo et al (2016); Crocce et al (2016); Banerji et al (2015); Sánchez et al (2014); Melchior et al (2016);Dark Energy Survey Collaboration et al (2016) and others show the very good quality of the SVA1 data. In Fig.…”

Section: Des Science Verification Datamentioning

confidence: 95%

Photometric redshifts and clustering of emission line galaxies selected jointly by DES and eBOSS

Jouvel

Delubac

Comparat

et al. 2017

Monthly Notices of the Royal Astronomical Society

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We present the results of the first test plates of the extended Baryon Oscillation Spectroscopic Survey. This paper focuses on the emission line galaxies (ELG) population targetted from the Dark Energy Survey (DES) photometry. We analyse the success rate, efficiency, redshift distribution, and clustering properties of the targets. From the 9000 spectroscopic redshifts targetted, 4600 have been selected from the DES photometry. The total success rate for redshifts between 0.6 and 1.2 is 71% and 68% respectively for a bright and faint, on average more distant, samples including redshifts measured from a single strong emission line. We find a mean redshift of 0.8 and 0.87, with 15 and 13% of unknown redshifts respectively for the bright and faint samples. In the redshift range 0.6 < z < 1.2, for the most secure spectroscopic redshifts, the mean redshift for the bright and faint sample is 0.85 and 0.9 respectively. Star contamination is lower than 2%. We measure a galaxy bias averaged on scales of 1 and 10 Mpc/h of 1.72±0.1 for the bright sample and of 1.78±0.12 for the faint sample. The error on the galaxy bias have been obtained propagating the errors in the correlation function to the fitted parameters. This redshift evolution for the galaxy bias is in agreement with theoretical expectations for a galaxy population with M B − 5 log h < −21.0. We note that biasing is derived from the galaxy clustering relative to a model for the mass fluctuations. We investigate the quality of the DES photometric redshifts and find that the outlier fraction can be reduced using a comparison between template fitting and neural network, or using a random forest algorithm.

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Combining Dark Energy Survey Science Verification data with near-infrared data from the ESO VISTA Hemisphere Survey

Cited by 32 publications

References 55 publications

OzDES multifibre spectroscopy for the Dark Energy Survey: 3-yr results and first data release

OzDES multifibre spectroscopy for the Dark Energy Survey: 3-yr results and first data release

A Study of Quasar Selection in the Supernova Fields of the Dark Energy Survey

Photometric redshifts and clustering of emission line galaxies selected jointly by DES and eBOSS

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