scholarly journals The VIMOS VLT Deep Survey final data release: a spectroscopic sample of 35 016 galaxies and AGN out toz~ 6.7 selected with 17.5 ≤iAB ≤ 24.75

2013 ◽  
Vol 559 ◽  
pp. A14 ◽  
Author(s):  
O. Le Fèvre ◽  
P. Cassata ◽  
O. Cucciati ◽  
B. Garilli ◽  
O. Ilbert ◽  
...  
Keyword(s):  
2020 ◽  
Vol 493 (2) ◽  
pp. 2568-2595
Author(s):  
S K Leggett ◽  
Nicholas J G Cross ◽  
Nigel C Hambly

ABSTRACT The currently defined ‘United Kingdom Infrared Telescope (UKIRT) Faint Standards’ have JHK magnitudes between 10 and 15, with Kmedian = 11.2. These stars will be too bright for the next generation of large telescopes. We have used multi-epoch observations taken as part of the UKIRT Infrared Deep Sky Survey (UKIDSS) and the Visible and Infrared Survey Telescope for Astronomy (VISTA) surveys to identify non-variable stars with JHK magnitudes in the range 16–19. The stars were selected from the UKIDSS Deep Extragalactic Survey and Ultra Deep Survey, the WFCAM calibration data (WFCAMCAL08B), the VISTA Deep Extragalactic Observations (VIDEO), and UltraVISTA. Sources selected from the near-infrared databases were paired with the Pan-STARRS Data Release 2 of optical to near-infrared photometry and the Gaia astrometric Data Release 2. Colour indices and other measurements were used to exclude sources that did not appear to be simple single stars. From an initial selection of 169 sources, we present a final sample of 81 standard stars with ZYJHK magnitudes, or a subset, each with 20 to 600 observations in each filter. The new standards have Ksmedian = 17.5. The relative photometric uncertainty for the sample is <0.006 mag and the absolute uncertainty is estimated to be ≲ 0.02 mag. The sources are distributed equatorially and are accessible from both hemispheres.


2019 ◽  
Vol 487 (2) ◽  
pp. 2061-2069 ◽  
Author(s):  
Tao Hong ◽  
Lister Staveley-Smith ◽  
Karen L Masters ◽  
Christopher M Springob ◽  
Lucas M Macri ◽  
...  

2016 ◽  
Vol 456 (4) ◽  
pp. 4156-4173 ◽  
Author(s):  
Francisco-Shu Kitaura ◽  
Sergio Rodríguez-Torres ◽  
Chia-Hsun Chuang ◽  
Cheng Zhao ◽  
Francisco Prada ◽  
...  
Keyword(s):  

2014 ◽  
Vol 447 (1) ◽  
pp. 234-245 ◽  
Author(s):  
Martin White ◽  
Beth Reid ◽  
Chia-Hsun Chuang ◽  
Jeremy L. Tinker ◽  
Cameron K. McBride ◽  
...  

2020 ◽  
Vol 499 (1) ◽  
pp. 210-229 ◽  
Author(s):  
Richard Neveux ◽  
Etienne Burtin ◽  
Arnaud de Mattia ◽  
Alex Smith ◽  
Ashley J Ross ◽  
...  

ABSTRACT We measure the clustering of quasars of the final data release (DR16) of eBOSS. The sample contains $343\, 708$ quasars between redshifts 0.8 ≤ z ≤ 2.2 over $4699\, \mathrm{deg}^2$. We calculate the Legendre multipoles (0,2,4) of the anisotropic power spectrum and perform a BAO and a Full-Shape (FS) analysis at the effective redshift zeff = 1.480. The errors include systematic errors that amount to 1/3 of the statistical error. The systematic errors comprise a modelling part studied using a blind N-body mock challenge and observational effects studied with approximate mocks to account for various types of redshift smearing and fibre collisions. For the BAO analysis, we measure the transverse comoving distance DM(zeff)/rdrag = 30.60 ± 0.90 and the Hubble distance DH(zeff)/rdrag = 13.34 ± 0.60. This agrees with the configuration space analysis, and the consensus yields: DM(zeff)/rdrag = 30.69 ± 0.80 and DH(zeff)/rdrag = 13.26 ± 0.55. In the FS analysis, we fit the power spectrum using a model based on Regularised Perturbation Theory, which includes redshift space distortions and the Alcock–Paczynski effect. The results are DM(zeff)/rdrag = 30.68 ± 0.90 and DH(zeff)/rdrag = 13.52 ± 0.51 and we constrain the linear growth rate of structure f(zeff)σ8(zeff) = 0.476 ± 0.047. Our results agree with the configuration space analysis. The consensus analysis of the eBOSS quasar sample yields: DM(zeff)/rdrag = 30.21 ± 0.79, DH(zeff)/rdrag = 3.23 ± 0.47, and f(zeff)σ8(zeff) = 0.462 ± 0.045 and is consistent with a flat ΛCDM cosmological model using Planck results.


2020 ◽  
Author(s):  
I Schroder ◽  
P de Caritat ◽  
L Wallace ◽  
J Trihey ◽  
C Boreham ◽  
...  

2013 ◽  
Vol 436 (1) ◽  
pp. 34-70 ◽  
Author(s):  
Barbara Catinella ◽  
David Schiminovich ◽  
Luca Cortese ◽  
Silvia Fabello ◽  
Cameron B. Hummels ◽  
...  

2007 ◽  
Vol 376 (1) ◽  
pp. L20-L24 ◽  
Author(s):  
S. Foucaud ◽  
O. Almaini ◽  
I. Smail ◽  
C. J. Conselice ◽  
K. P. Lane ◽  
...  

2021 ◽  
Vol 503 (1) ◽  
pp. 1149-1173 ◽  
Author(s):  
Cheng Zhao ◽  
Chia-Hsun Chuang ◽  
Julian Bautista ◽  
Arnaud de Mattia ◽  
Anand Raichoor ◽  
...  

ABSTRACT We produce 1000 realizations of synthetic clustering catalogues for each type of the tracers used for the baryon acoustic oscillation and redshift space distortion analysis of the Sloan Digital Sky Surveys-iv extended Baryon Oscillation Spectroscopic Survey final data release (eBOSS DR16), covering the redshift range from 0.6 to 2.2, to provide reliable estimates of covariance matrices and test the robustness of the analysis pipeline with respect to observational systematics. By extending the Zel’dovich approximation density field with an effective tracer bias model calibrated with the clustering measurements from the observational data, we accurately reproduce the two- and three-point clustering statistics of the eBOSS DR16 tracers, including their cross-correlations in redshift space with very low computational costs. In addition, we include the gravitational evolution of structures and sample selection biases at different redshifts, as well as various photometric and spectroscopic systematic effects. The agreements on the auto-clustering statistics between the data and mocks are generally within $1\, \sigma$ variances inferred from the mocks, for scales down to a few $h^{-1}\, {\rm Mpc}$ in configuration space, and up to $0.3\, h\, {\rm Mpc}^{-1}$ in Fourier space. For the cross correlations between different tracers, the same level of consistency presents in configuration space, while there are only discrepancies in Fourier space for scales above $0.15\, h\, {\rm Mpc}^{-1}$. The accurate reproduction of the data clustering statistics permits reliable covariances for multi-tracer analysis.


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