The 2dF-SDSS LRG and QSO (2SLAQ) Luminous Red Galaxy Survey

Cannon, R. D.; Drinkwater, Michael J.; Edge, A. C.; Eisenstein, Daniel J.; Nichol, R. C.; Outram, P. J.; Pimbblet, Kevin A.; Propris, Roberto De; Roseboom, I. G.; Wake, David A.; Allen, Paul; Bland-Hawthorn, Joss; Bridges, Terry J.; Carson, Daniel J.; Chiu, Kuenley; Colless, Matthew; Couch, Warrick J.; Croom, S. M.; Driver, Simon P.; Fine, S.; Hewett, Paul C.; Loveday, Jon; Ross, Nicholas P.; Sadler, E. M.; Shanks, T.; Sharp, Robert; Smith, J. Allyn; Stoughton, Chris; Weilbacher, Peter M.; Brunner, Róbert; Meiksin, Avery; Schneider, Donald P.

doi:10.1111/j.1365-2966.2006.10875.x

Cited by 165 publications

(161 citation statements)

References 28 publications

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“…For the majority of the 2SLAQ survey, the criteria i deV ≤ 19.8 and d ⊥ ≥ 0.55 were used. For further details on this see Cannon et al (2006). However, for the full MegaZ-LRG sample described in Collister et al (2007), the flux limit is i deV ≤ 20, which means that roughly 1/3 of the sample is fainter than the 2SLAQ flux limit.…”

Section: Megaz-lrgmentioning

confidence: 99%

“…For a subset of the galaxies, spectroscopic redshift information is required for calibration and cross-checking, which is provided by the 2SLAQ survey (Cannon et al 2006). Consequently, the selection criteria of MegaZ-LRG have been designed to match those of 2SLAQ, using a series of magnitude and colour cuts (for details see Cannon et al 2006;Collister et al 2007). These criteria have an efficiency of 95 % in detecting LRGs in the redshift range 0.4 ≤ z ≤ 0.7, the failures being almost entirely due to Mtype stars.…”

Section: Megaz-lrgmentioning

confidence: 99%

“…Hirata et al (2007) extended the analysis to SDSS luminous red galaxies (LRGs) at slightly higher redshift (0.15 < z < 0.35), finding evidence for a dependence on galaxy type and for an increase of the intrinsic alignment signal of early-type galaxies with luminosity. They also considered a small sample of galaxies at intermediate redshifts (z ∼ 0.5) from the 2dF-SDSS LRG and Quasar Survey (2SLAQ, Cannon et al 2006), but only marginally detected a signal in a bright subsample due to poor statistics. Recently, Mandelbaum et al (2010) increased the redshift baseline for position-shape correlation measurements of blue galaxies out to z ∼ 0.7 by incorporating a large number of spectroscopic redshifts from the WiggleZ Survey (Drinkwater et al 2010), reporting a null detection for all subsamples.…”

Section: Introductionmentioning

confidence: 99%

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Constraints on intrinsic alignment contamination of weak lensing surveys using the MegaZ-LRG sample

Joachimi¹,

Mandelbaum²,

Abdalla³

et al. 2011

A&A

284

512

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Correlations between the intrinsic shapes of galaxies and the large-scale galaxy density field provide an important tool to investigate galaxy intrinsic alignments, which constitute the major potential astrophysical systematic in cosmological weak lensing (cosmic shear) surveys, but also yield insight into the formation and evolution of galaxies. We measure galaxy position-shape correlations in the MegaZ-LRG sample for more than 800 000 luminous red galaxies for comoving transverse separations of 0.3 < r p < 60 h −1 Mpc, making the first such measurement with a photometric redshift sample. In combination with a re-analysis of several spectroscopic SDSS samples, we constrain an intrinsic alignment model for early-type galaxies over long baselines in redshift (z 0.7) and luminosity (4 mag) with high statistical precision. We develop and test the formalism to incorporate photometric redshift scatter in the modelling of these observations. For r p > 6 h −1 Mpc, the fits to galaxy position-shape correlation functions are consistent with the scaling with r p and redshift of a revised, nonlinear version of the linear alignment model for all samples. An extra redshift dependence ∝ (1 + z) η other is constrained to η other = −0.3 ± 0.8 (1σ). To obtain consistent amplitudes for all data, an additional dependence on galaxy luminosity ∝L β with β = 1.1 +0.3 −0.2 is required. The normalisation of the intrinsic alignment power spectrum is found to be (0.077 ± 0.008) ρ −1 cr for galaxies at redshift 0.3 and r band magnitude of −22 (k-and evolution-corrected to z = 0). Assuming zero intrinsic alignments for blue galaxies, we assess the bias on cosmological parameters for a tomographic CFHTLSlike lensing survey given our new constraints on the intrinsic alignment model parameter space. Both the resulting mean bias and its uncertainty are smaller than the 1σ statistical errors when using the constraints from all samples combined. The addition of MegaZ-LRG data is critical to achieving constraints this strong, reducing the uncertainty in intrinsic alignment bias on cosmological parameters by factors of three to seven.

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Section: Megaz-lrgmentioning

confidence: 99%

Section: Megaz-lrgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Constraints on intrinsic alignment contamination of weak lensing surveys using the MegaZ-LRG sample

Joachimi¹,

Mandelbaum²,

Abdalla³

et al. 2011

A&A

284

512

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“…The source catalogues are supplemented with cross-identification information from the GAMA survey (Driver et al 2009) and SDSS DR-7 (Abazajian et al 2009) as described in Smith et al (in prep.). For the sources for which spectroscopic redshifts from GAMA or SDSS are not available, we use photometric redshifts generated using the ANNz neural network code (Collister & Lahav 2004), trained with photometry from SDSS and UKIDSS LAS (Lawrence et al 2007), and spectra from the GAMA spectroscopic survey (Driver et al 2009), DEEP2 (Davis et al 2007), zCOSMOS (Lilly et al 2007), and the 2SLAQ-LRG (Cannon et al 2006) survey.…”

Section: Colour-colour Diagramsmentioning

confidence: 99%

Herschel-ATLAS: Dust temperature and redshift distribution of SPIRE and PACS detected sources using submillimetre colours

Amblard¹,

Cooray²,

Serra³

et al. 2010

A&A

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111

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We present colour-colour diagrams of detected sources in the Herschel-ATLAS science demonstration field from 100 to 500 μm using both PACS and SPIRE. We fit isothermal modified black bodies to the spectral energy distribution (SED) to extract the dust temperature of sources with counterparts in Galaxy And Mass Assembly (GAMA) or SDSS surveys with either a spectroscopic or a photometric redshift. For a subsample of 330 sources detected in at least three FIR bands with a significance greater than 3σ, we find an average dust temperature of (28 ± 8) K. For sources with no known redshift, we populate the colour-colour diagram with a large number of SEDs generated with a broad range of dust temperatures and emissivity parameters, and compare to colours of observed sources to establish the redshift distribution of this sample. For another subsample of 1686 sources with fluxes above 35 mJy at 350 μm and detected at 250 and 500 μm with a significance greater than 3σ, we find an average redshift of 2.2 ± 0.6.

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“…Photometric redshifts have been estimated using a training sample constructed from spectroscopic redshifts from GAMA I, SDSS DR7, 2SLAQ (Cannon et al 2006), AEGIS (Davis et al 2007), and zCOSMOS (Lilly et al 2009), covering redshifts z < 1. Of these sources, 5500 lie in the X-ATLAS region, and 3515 have a photometric redshift estimate using ANNz.…”

Section: Summary and Discussionmentioning

confidence: 99%

Estimating photometric redshifts for X-ray sources in the X-ATLAS field using machine-learning techniques

Mountrichas

Corral

Masoura

et al. 2017

A&A

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We present photometric redshifts for 1031 X-ray sources in the X-ATLAS field using the machine-learning technique TPZ. X-ATLAS covers 7.1 deg 2 observed with XMM-Newton within the Science Demonstration Phase of the H-ATLAS field, making it one of the largest contiguous areas of the sky with both XMM-Newton and Herschel coverage. All of the sources have available SDSS photometry, while 810 additionally have mid-IR and/or near-IR photometry. A spectroscopic sample of 5157 sources primarily in the XMM/XXL field, but also from several X-ray surveys and the SDSS DR13 redshift catalogue, was used to train the algorithm. Our analysis reveals that the algorithm performs best when the sources are split, based on their optical morphology, into point-like and extended sources. Optical photometry alone is not enough to estimate accurate photometric redshifts, but the results greatly improve when at least mid-IR photometry is added in the training process. In particular, our measurements show that the estimated photometric redshifts for the X-ray sources of the training sample have a normalized absolute median deviation, nmad ≈ 0.06, and a percentage of outliers, η = 10-14%, depending upon whether the sources are extended or point like. Our final catalogue contains photometric redshifts for 933 out of the 1031 X-ray sources with a median redshift of 0.9.

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The 2dF-SDSS LRG and QSO (2SLAQ) Luminous Red Galaxy Survey

Cited by 165 publications

References 28 publications

Constraints on intrinsic alignment contamination of weak lensing surveys using the MegaZ-LRG sample

Constraints on intrinsic alignment contamination of weak lensing surveys using the MegaZ-LRG sample

Herschel-ATLAS: Dust temperature and redshift distribution of SPIRE and PACS detected sources using submillimetre colours

Estimating photometric redshifts for X-ray sources in the X-ATLAS field using machine-learning techniques

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