Handbook for the GREAT08 Challenge: An image analysis competition for cosmological lensing

Bridle, Sarah; Shawe‐Taylor, John; Amara, Adam; Applegate, Douglas; Balan, Sreekumar T.; Bergé, Joël; Bernstein, G. M.; Dahle, H.; Erben, T.; Gill, M. S.; Heavens, Alan; Heymans, Catherine; High, F. W.; Hoekstra, Henk; Jarvis, Mike; Kirk, D.; Kitching, Thomas D.; Kneib, Jean-Paul; Kuijken, Konrad; Lagatutta, David; Mandelbaum, Rachel; Massey, Richard; Mellier, Y.; Moghaddam, B.; Moudden, Y.; Nakajima, Reiko; Paulin-Henriksson, S.; Pires, S.; Rassat, A.; Réfrégier, Alexandre; Rhodes, Jason; Schrabback, T.; Semboloni, Elisabetta; Shmakova, Marina; Waerbeke, Ludovic Van; Witherick, D. K.; Voigt, Lisa; Wittman, David

doi:10.1214/08-aoas222

Cited by 103 publications

(107 citation statements)

References 45 publications

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“…This evolution has been demonstrated through a series of community challenges (Heymans et al 2006;Massey et al 2007b;Bridle et al 2009Bridle et al , 2010Kitching et al 2010Kitching et al , 2012Mandelbaum et al 2014Mandelbaum et al , 2015. In the most recent of these, GREAT3, the types of methods used included those based on estimation of per-galaxy shapes via measurements of moments, fitting parametric light profiles, decomposition into basis functions, and machine learning, as well as methods that involve inferring ensemble shears without per-galaxy shapes.…”

Section: Shear Estimation Algorithmmentioning

confidence: 99%

The first-year shear catalog of the Subaru Hyper Suprime-Cam Subaru Strategic Program Survey

Mandelbaum

Miyatake

Hamana

et al. 2017

Publications of the Astronomical Society of Japan

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We present and characterize the catalog of galaxy shape measurements that will be used for cosmological weak lensing measurements in the Wide layer of the first year of the Hyper Suprime-Cam (HSC) survey. The catalog covers an area of 136.9 deg 2 split into six fields, with a mean i-band seeing of 0.58 ′′ and 5σ point-source depth of i ∼ 26. Given conservative galaxy selection criteria for first year science, the depth and excellent image quality results in unweighted and weighted source number densities of 24.6 and 21.8 arcmin −2 , respectively. We define the requirements for cosmological weak lensing science with this catalog, then focus on characterizing potential systematics in the catalog using a series of internal null tests for problems with point-spread function (PSF) modeling, shear estimation, and other aspects of the image processing. We find that the PSF models narrowly meet requirements for weak lensing science with this catalog, with fractional PSF model size residuals of approximately 0.003 (requirement: 0.004) and the PSF model shape correlation function ρ 1 < 3 × 10 −7 (requirement:A variety of galaxy shape-related null tests are statistically consistent with zero, but star-galaxy shape correlations reveal additive systematics on on > 1• scales that are sufficiently large as to require mitigation in cosmic shear measurements. Finally, we discuss the dominant systematics and the planned algorithmic changes to reduce them in future data reductions.

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Section: Shear Estimation Algorithmmentioning

confidence: 99%

The first-year shear catalog of the Subaru Hyper Suprime-Cam Subaru Strategic Program Survey

Mandelbaum

Miyatake

Hamana

et al. 2017

Publications of the Astronomical Society of Japan

Self Cite

228

195

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“…Mellier 1999;Bartelmann & Schneider 2001;Refregier 2003;Hoekstra & Jain 2008;Munshi et al 2008) and has been shown to be the most promising probe of dark energy (Albrecht et al 2006;Peacock et al 2006). Surveys' optimization and systematics minimizations in both software and hardware have been investigated (Heymans et al 2006;Massey et al 2007a;Amara & Réfrégier 2007, 2008Paulin-Henriksson et al 2008;Amara et al 2009;Bridle et al 2009), favoring wide surveys, with well-controlled, stable Point Spread Function, with comprehensive photometric redshift follow-up. Those characteristics are shared by ambitious upcoming large area surveys, such as the Large Synoptic Survey Telescope (LSST) 5 , the Panoramic Survey Telescope & Rapid Response System (Pan-STARRS) 6 , Euclid 7 and the Joint Dark Energy Mission (JDEM) 8 .…”

Section: Introductionmentioning

confidence: 99%

Optimal Capture of Non-Gaussianity in Weak-Lensing Surveys: Power Spectrum, Bispectrum, and Halo Counts

Bergé

Amara

Réfrégier

2010

ApJ

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We compare the efficiency of weak lensing-selected galaxy clusters counts and of the weak lensing bispectrum at capturing non-Gaussian features in the dark matter distribution. We use the halo model to compute the weak lensing power spectrum, the bispectrum and the expected number of detected clusters, and derive constraints on cosmological parameters for a large, low systematic weak lensing survey, by focusing on the Ω m -σ 8 plane and on the dark energy equation of state. We separate the power spectrum into the resolved and the unresolved parts of the data, the resolved part being defined as detected clusters, and the unresolved part as the rest of the field. We consider four kinds of clusters counts, taking into account different amount of information : signal-to-noise ratio peak counts; counts as a function of clusters' mass; counts as a function of clusters' redshift; and counts as a function of clusters' mass and redshift. We show that when combined with the power spectrum, those four kinds of counts provide similar constraints, thus allowing one to perform the most direct counts, signalto-noise peaks counts, and get percent level constraints on cosmological parameters. We show that the weak lensing bispectrum gives constraints comparable to those given by the power spectrum and captures non-Gaussian features as well as clusters counts, its combination with the power spectrum giving errors on cosmological parameters that are similar to, if not marginally smaller than, those obtained when combining the power spectrum with cluster counts. We finally note that in order to reach its potential, the weak lensing bispectrum must be computed using all triangle configurations, as equilateral triangles alone do not provide useful information. The appendices summarize the halo model, and the way the power spectrum and bispectrum are computed in this framework.

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“…The GREAT10 challenge is the continuation of the GREAT08 challenge (Bridle et al 2009(Bridle et al , 2010 and STEP programs (Heymans et al 2006;Massey et al 2007;Bridle et al 2010), with an increasing degree of complexity.…”

Section: The Great10 Galaxy Challenge and Its Datasetmentioning

confidence: 99%

Hopfield neural network deconvolution for weak lensing measurement

Nurbaeva¹,

Tewes²,

Courbin³

et al. 2015

A&A

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Weak gravitational lensing has the potential to place tight constraints on the equation of the state of dark energy. However, this will only be possible if shear measurement methods can reach the required level of accuracy. We present a new method for measuring the ellipticity of galaxies used in weak lensing surveys. The method makes use of direct deconvolution of the data by the total point spread function (PSF). We adopt a linear algebra formalism that represents the PSF as a Toeplitz matrix. This allows us to solve the convolution equation by applying the Hopfield neural network iterative scheme. The ellipticity of galaxies in the deconvolved images are then measured using second-order moments of the autocorrelation function of the images. To our knowledge, it is the first time full image deconvolution has been used to measure weak lensing shear. We apply our method to the simulated weak lensing data proposed in the GREAT10 challenge and obtain a quality factor of Q = 87. This result is obtained after applying image denoising to the data, prior to the deconvolution. The additive and multiplicative biases on the shear power spectrum are then √ A = +0.09 × 10 −4and M/2 = +0.0357, respectively.

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Handbook for the GREAT08 Challenge: An image analysis competition for cosmological lensing

Cited by 103 publications

References 45 publications

The first-year shear catalog of the Subaru Hyper Suprime-Cam Subaru Strategic Program Survey

The first-year shear catalog of the Subaru Hyper Suprime-Cam Subaru Strategic Program Survey

Optimal Capture of Non-Gaussianity in Weak-Lensing Surveys: Power Spectrum, Bispectrum, and Halo Counts

Hopfield neural network deconvolution for weak lensing measurement

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