Combined analysis of weak lensing and X-ray blind surveys

Bergé, Joël; Pacaud, F.; Réfrégier, Alexandre; Massey, Richard; Pierre, M.; Amara, Adam; Birkinshaw, Mark; Paulin-Henriksson, S.; Smith, Graham P.; Willis, Jon

doi:10.1111/j.1365-2966.2008.12902.x

Cited by 47 publications

(36 citation statements)

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“…We also show in P1 that the polar shapelet basis set, proposed by Massey & Refregier (2005), tested on simulated data in Massey et al (2007) and used on real data by Bergé et al (2008), is particularly convenient for modeling the PSF. For example, in the small ellipticity regime, ψ and ψ R 2 depend only on the polar shapelet basis set over which the PSF is decomposed, not on the PSF itself.…”

Section: Complexitymentioning

confidence: 99%

Optimal point spread function modeling for weak lensing: complexity and sparsity

Paulin-Henriksson

Réfrégier

Amara

2009

A&A

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Context. We address the issue of controling the systematic errors in shape measurements for weak gravitational lensing. Aims. We make a step to quantify the impact of systematic errors in modeling the point spread function (PSF) of observations, on the determination of cosmological parameters from cosmic shear. Methods. We explore the impact of PSF fitting errors on cosmic shear measurements using the concepts of complexity and sparsity. Complexity, introduced in a previous paper, characterizes the number of degrees of freedom of the PSF. For instance, fitting an underlying PSF with a model of low complexity produces small statistical errors on the model parameters, although these parameters could be affected by large biases. Alternatively, fitting a large number of parameters (i.e. a high complexity) tends to reduce biases at the expense of increasing the statistical errors. We attempt to find a trade-off between scatters and biases by studying the mean squared error of a PSF model. We also characterize the model sparsity, which describes how efficiently the model is able to represent the underlying PSF using a limited number of free parameters. We present the general case and give an illustration for a realistic example of a PSF modeled by a shapelet basis set. Results. We derive a relation between the complexity and the sparsity of the PSF model, the signal-to-noise ratio of stars and the systematic errors in the cosmological parameters. By insisting that the systematic errors are below the statistical uncertainties, we derive a relation between the number of stars required to calibrate the PSF and the sparsity of the PSF model. We discuss the impact of our results for current and future cosmic shear surveys. In the typical case where the sparsity can be represented by a power-law function of the complexity, we demonstrate that current ground-based surveys can calibrate the PSF with few stars, while future surveys will require hard constraints on the sparsity in order to calibrate the PSF with 50 stars.

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Section: Complexitymentioning

confidence: 99%

Optimal point spread function modeling for weak lensing: complexity and sparsity

Paulin-Henriksson

Réfrégier

Amara

2009

A&A

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“…It is now evident that the exploration of large-scale structure and the high-redshift Universe with the Canada France Hawaii Telescope Legacy Survey (CFHTLS) requires precise magnitudes and redshifts for millions of sources (Benjamin et al 2007;Parker et al 2007;McCracken et al 2008;Fu et al 2008;Bergé et al 2008;Kilbinger et al 2009;Tereno et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Photometric redshifts for the CFHTLS T0004 deep and wide fields

Coupon¹,

Ilbert

Kilbinger³

et al. 2009

A&A

188

303

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Aims. We compute photometric redshifts in the fourth public release of the Canada-France-Hawaii Telescope Legacy Survey. This unique multi-colour catalogue comprises u * , g , r , i , z photometry in four deep fields of 1 deg 2 each and 35 deg 2 distributed over three wide fields. Methods. We used a template-fitting method to compute photometric redshifts calibrated with a large catalogue of 16 983 high-quality spectroscopic redshifts from the VVDS-F02, VVDS-F22, DEEP2, and the zCOSMOS surveys. The method includes correction of systematic offsets, template adaptation, and the use of priors. We also separated stars from galaxies using both size and colour information.Results. Comparing with galaxy spectroscopic redshifts, we find a photometric redshift dispersion, σ Δz/(1+zs) , of 0.028-0.30 and an outlier rate, |Δz| ≥ 0.15 × (1 + z s ), of 3-4% in the deep field at i AB < 24. In the wide fields, we find a dispersion of 0.037-0.039 and an outlier rate of 3-4% at i AB < 22.5. Beyond i AB = 22.5 in the wide fields the number of outliers rises from 5% to 10% at i AB < 23 and i AB < 24, respectively. For the wide sample the systematic redshift bias stays below 1% to i AB < 22.5, whereas we find no significant bias in the deep fields. We investigated the effect of tile-to-tile photometric variations and demonstrated that the accuracy of our photometric redshifts is reduced by at most 21%. Application of our star-galaxy classifier reduced the contamination by stars in our catalogues from 60% to 8% at i AB < 22.5 in our field with the highest stellar density while keeping a complete galaxy sample. Our CFHTLS T0004 photometric redshifts are distributed to the community. Our release includes 592 891 (i AB < 22.5) and 244 701 (i AB < 24) reliable galaxy photometric redshifts in the wide and deep fields, respectively.

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“…Böhringer et al 2000;Finoguenov et al 2007a;Vikhlinin et al 2009), observation of weak-lensing distortions of background galaxies (e.g. Marian & Bernstein 2006;Gavazzi & Soucail 2007;Massey et al 2007;Bergé et al 2008).…”

Section: Introductionmentioning

confidence: 99%

SARCS strong-lensing galaxy groups

Foëx¹,

Motta²,

Limousin³

et al. 2013

A&A

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We present the weak-lensing and optical analysis of the SL2S-ARCS (SARCS) sample of strong-lensing candidates. The sample is based on the Strong Lensing Legacy Survey (SL2S), a systematic search of strong-lensing systems in the photometric CanadaFrance-Hawaii Telescope Legacy Survey (CFHTLS). The SARCS sample focusses on arc-like features and is designed to contain mostly galaxy groups. We briefly present the weak-lensing methodology that we used to estimate the mass of the SARCS objects. Among 126 candidates, we obtained a weak-lensing detection (at the 1σ level) for 89 objects with velocity dispersions of the singular isothermal sphere mass model (SIS) ranging from σ SIS ∼ 350 km s −1 to ∼1000 km s −1 with an average value of σ SIS ∼ 600 km s −1 , corresponding to a rich galaxy group (or poor cluster). From the galaxies belonging to the bright end of the group's red sequence (M i < −21), we derived the optical properties of the SARCS candidates. We obtained typical richnesses of N ∼ 5−15 galaxies and optical luminosities of L ∼ 0.5−1.5 × 10 12 L (within a radius of 0.5 Mpc). We used these galaxies to compute luminosity density maps, from which a morphological classification reveals that a large fraction of the sample (∼45%) are groups with a complex light distribution, either elliptical or multi-modal, suggesting that these objects are dynamically young structures. We finally combined the lensing and optical analyses to define a sample of the 80 most secure group candidates, i.e. weak-lensing detection and over-density at the lens position in the luminosity map, to remove false detections and galaxy-scale systems from the initial sample. We use this reduced sample to probe the optical scaling relations in combination with a sample of massive galaxy clusters. We detect the expected correlations over the probed range in mass with a typical scatter of ∼25% in σ SIS at a given richness or luminosity, making these scaling laws interesting mass proxies.

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Combined analysis of weak lensing and X-ray blind surveys

Cited by 47 publications

References 100 publications

Optimal point spread function modeling for weak lensing: complexity and sparsity

Optimal point spread function modeling for weak lensing: complexity and sparsity

Photometric redshifts for the CFHTLS T0004 deep and wide fields

SARCS strong-lensing galaxy groups

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