Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data

Kacprzak, T.; Kirk, D.; Friedrich, Oliver; Amara, Adam; Réfrégier, Alexandre; Marian, Laura; Dietrich, J. P.; Suchyta, E.; Aleksić, J.; Bacon, David; Becker, M. R.; Bonnett, C.; Bridle, Sarah; Chang, C.; Eifler, T. F.; Hartley, W. G.; Huff, Eric; Krause, E.; MacCrann, N.; Melchior, P.; Nicola, Andrina; Samuroff, S.; Sheldon, E.; Troxel, M. A.; Weller, J.; Zuntz, Joe; Abbott, T. M. C.; Abdalla, F. B.; Armstrong, R.; Benoit-Lévy, A.; Bernstein, G. M.; Bernstein, R. A.; Bertin, E.; Brooks, D.; Burke, D. L.; Rosell, A. Carnero; Kind, M. Carrasco; Carretero, J.; Castander, F. J.; Crocce, M.; D’Andrea, C. B.; Costa, L. N. da; Desai, S.; Diehl, H. T.; Evrard, A. E.; Neto, A. Fausti; Flaugher, B.; Fosalba, P.; Frieman, J.; Gerdes, D. W.; Goldstein, D.; Gruen, David; Gruendl, R. A.; Gutiérrez, G.; Honscheid, K.; Jain, Bhuvnesh; James, D. J.; Jarvis, Mike; Kuêhn, K.; Kuropatkin, N.; Lahav, O.; Lima, M.; March, M.; Marshall, J. L.; Martini, Paul; Miller, C. J.; Miquel, R.; Mohr, J. J.; Nichol, R. C.; Nord, B.; Plazas, A. A.; Romer, A. K.; Roodman, A.; Rykoff, E. S.; Sánchez, E.; Scarpine, V.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Swanson, M. E. C.; Tarlè, G.; Thomas, Daniel; Vikram, V.; Walker, A. R.; Zhang, Y.

doi:10.1093/mnras/stw2070

Cited by 148 publications

(158 citation statements)

References 113 publications

(94 reference statements)

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“…The same is true for other large-scale structure probes not shown in Fig. 4 such as the CFHTlens weak lensing shear survey (Heymans et al 2013), the peak statistics of lensing (Liu et al 2015;Kacprzak et al 2016), or the Sunyaev-Zeldovich cluster counts (Ade et al 2016). The contours from Planck15 (Ade et al 2015), on the other hand, do not agree with the observed velocity function at the 2-σ level.…”

Section: Cosmological Parameter Estimationmentioning

confidence: 70%

Constraining cosmology with the velocity function of low-mass galaxies

Schneider

Trujillo-Gomez

2018

Monthly Notices of the Royal Astronomical Society

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The number density of field galaxies per rotation velocity, referred to as the velocity function, is an intriguing statistical measure probing the smallest scales of structure formation. In this paper we point out that the velocity function is sensitive to small shifts in key cosmological parameters such as the amplitude of primordial perturbations (σ 8 ) or the total matter density (Ω m ). Using current data and applying conservative assumptions about baryonic effects, we show that the observed velocity function of the Local Volume favours cosmologies in tension with the measurements from Planck but in agreement with the latest findings from weak lensing surveys. While the current systematics regarding the relation between observed and true rotation velocities are potentially important, upcoming data from HI surveys as well as new insights from hydrodynamical simulations will dramatically improve the situation in the near future.

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Section: Cosmological Parameter Estimationmentioning

confidence: 70%

Constraining cosmology with the velocity function of low-mass galaxies

Schneider

Trujillo-Gomez

2018

Monthly Notices of the Royal Astronomical Society

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“…WL peaks, on the other hand, are high signal regions, that are closely associated with massive structures along the line-of-sight (LOS). Their statistics is a simple and effective way to capture the non-Gaussian information in the WL field, and thus highly complementary to the cosmic shear 2PCF (e.g., Kruse & Schneider 1999;Dietrich & Hartlap 2010;Shan et al 2012Shan et al , 2014Marian et al 2012Marian et al , 2013Lin & Kilbinger 2015;Martinet et al 2015;Hamana et al 2015;Liu et al, 2015aLiu et al, , b, 2016Kacprzak et al 2016).…”

mentioning

confidence: 99%

“…With the same method, Liu et al (2016b) presented constraints on the f(R) theory with the CFHTLenS data. Kacprzak et al (2016) measured the shear peaks using aperture mass maps (Schneider 1996;Bartelmann & Schneider 2001) from the Dark Energy Survey Science Verification (DES-SV) data. To derive cosmological constraints, they also adopted the simulation approach to produce WL maps (Dietrich & Hartlap 2010) spanning the (Ωm, σ8) plane.…”

mentioning

confidence: 99%

KiDS-450: cosmological constraints from weak lensing peak statistics – I. Inference from analytical prediction of high signal-to-noise ratio convergence peaks

Shan

Liu

Hildebrandt

et al. 2017

Monthly Notices of the Royal Astronomical Society

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This paper is the first of a series of papers constraining cosmological parameters with weak lensing peak statistics using ∼ 450 deg 2 of imaging data from the Kilo Degree Survey (KiDS-450). We measure high signal-to-noise ratio (SNR: ν) weak lensing convergence peaks in the range of 3 < ν < 5, and employ theoretical models to derive expected values. These models are validated using a suite of simulations. We take into account two major systematic effects, the boost factor and the effect of baryons on the mass-concentration relation of dark matter haloes. In addition, we investigate the impacts of other potential astrophysical systematics including the projection effects of large scale structures, intrinsic galaxy alignments, as well as residual measurement uncertainties in the shear and redshift calibration. Assuming a flat ΛCDM model, we find constraints for S 8 = σ 8 (Ω m /0.3) 0.5 = 0.746−0.107 according to the degeneracy direction of the cosmic shear analysis and−0.050 based on the derived degeneracy direction of our high-SNR peak statistics. The difference between the power index of S 8 and in Σ 8 indicates that combining cosmic shear with peak statistics has the potential to break the degeneracy in σ 8 and Ω m . Our results are consistent with the cosmic shear tomographic correlation analysis of the same dataset and ∼ 2σ lower than the Planck 2016 results.

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“…These include the lack of halo clustering, inaccurate modelling of the halo concentration, and the use of NFW profiles. Kacprzak et al (2016) have also argued that ignoring source clustering results is a boost factor to peak counts. See also the discussion in Sect.…”

Section: Parameter Constraints From Peak Countsmentioning

confidence: 99%

“…Since then, many other papers have explored the efficacy of peaks for constraining both standard and nonstandard cosmological models (Marian et al 2009(Marian et al , 2011(Marian et al , 2012(Marian et al , 2013Dietrich & Hartlap 2010;Maturi et al 2010Maturi et al , 2011Pires et al 2012;Cardone et al 2013;Lin & Kilbinger 2015a,b;Lin et al 2016). Regarding recent surveys, peak analyses have also been used to derive constraints on (σ 8 , Ω m ) with CFHTLenS data (Liu et al 2015a), CFHT Stripe-82 data (Liu et al 2015b), and DES Science Verification data (Kacprzak et al 2016). Euclid will observe approximately 15 000 deg 2 of the extragalactic sky, an area about two orders of magnitude larger than CFHTLenS and the currently available DES SV data.…”

Section: Introductionmentioning

confidence: 99%

Cosmological constraints with weak-lensing peak counts and second-order statistics in a large-field survey

Peel

Lin

Lanusse

et al. 2017

A&A

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Peak statistics in weak-lensing maps access the non-Gaussian information contained in the large-scale distribution of matter in the Universe. They are therefore a promising complementary probe to two-point and higher-order statistics to constrain our cosmological models. Next-generation galaxy surveys, with their advanced optics and large areas, will measure the cosmic weak-lensing signal with unprecedented precision. To prepare for these anticipated data sets, we assess the constraining power of peak counts in a simulated Euclid-like survey on the cosmological parameters Ω m , σ 8 , and w de 0 . In particular, we study how Camelus, a fast stochastic model for predicting peaks, can be applied to such large surveys. The algorithm avoids the need for time-costly N-body simulations, and its stochastic approach provides full PDF information of observables. Considering peaks with a signal-to-noise ratio ≥1, we measure the abundance histogram in a mock shear catalogue of approximately 5000 deg 2 using a multiscale mass-map filtering technique. We constrain the parameters of the mock survey using Camelus combined with approximate Bayesian computation, a robust likelihoodfree inference algorithm. Peak statistics yield a tight but significantly biased constraint in the σ 8 -Ω m plane, as measured by the width ∆Σ 8 of the 1σ contour. We find Σ 8 = σ 8 (Ω m /0.27) α = 0.77−0.05 with α = 0.75 for a flat ΛCDM model. The strong bias indicates the need to better understand and control the model systematics before applying it to a real survey of this size or larger. We perform a calibration of the model and compare results to those from the two-point correlation functions ξ ± measured on the same field. We calibrate the ξ ± result as well, since its contours are also biased, although not as severely as for peaks. In this case, we find for peaks Σ 8 = 0.76 −0.01 and α = 0.70. We conclude that the constraining power can therefore be comparable between the two weak-lensing observables in large-field surveys. Furthermore, the tilt in the σ 8 -Ω m degeneracy direction for peaks with respect to that of ξ ± suggests that a combined analysis would yield tighter constraints than either measure alone. As expected, w de 0 cannot be well constrained without a tomographic analysis, but its degeneracy directions with the other two varied parameters are still clear for both peaks and ξ ± .

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Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data

Cited by 148 publications

References 113 publications

Constraining cosmology with the velocity function of low-mass galaxies

Constraining cosmology with the velocity function of low-mass galaxies

KiDS-450: cosmological constraints from weak lensing peak statistics – I. Inference from analytical prediction of high signal-to-noise ratio convergence peaks

Cosmological constraints with weak-lensing peak counts and second-order statistics in a large-field survey

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