Effect of Measurement Errors on Predicted Cosmological Constraints From Shear Peak Statistics With Large Synoptic Survey Telescope

Bard, Deborah; Kratochvíl, Jan; Chang, C.; May, M.; Kahn, S. M.; AlSayyad, Yusra; Ahmad, Zahid; Bankert, J.; Connolly, Andrew J.; Gibson, R. R.; Gilmore, K.; Grace, E.; Haiman, Zoltán; Hannel, Mark; Huffenberger, K. M.; Jernigan, J. G.; Jones, Lynne; Krughoff, S.; Lorenz, Sebastian; Marshall, Stuart; Meert, A.; Nagarajan, S.; Peng, E.; Peterson, J. R.; Rasmussen, Andrew; Shmakova, Marina; Sylvestre, N.; Todd, N.; Young, Michael D.

doi:10.1088/0004-637x/774/1/49

Cited by 25 publications

(39 citation statements)

References 57 publications

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“…Another powerful advantage of our model is its flexibility. Additional effects such as intrinsic ellipticity alignment, alternative methods such as nonlinear filters, and realistic survey settings, such as mask effects, magnification bias (Liu et al 2014), shape measurement errors (Bard et al 2013), and photo-z errors, can all be modeled in this peak-counting framework. The forward-modeling approach allows for a straightforward inclusion and marginalization of model uncertainties and systematics.…”

Section: Summary and Perspectivesmentioning

confidence: 99%

A new model to predict weak-lensing peak counts

Lin

Kilbinger

2015

A&A

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Context. Weak-lensing peak counts have been shown to be a powerful tool for cosmology. They provide non-Gaussian information of large scale structures and are complementary to second-order statistics. Aims. We propose a new flexible method for predicting weak-lensing peak counts, which can be adapted to realistic scenarios, such as a real source distribution, intrinsic galaxy alignment, mask effects, and photo-z errors from surveys. The new model is also suitable for applying the tomography technique and nonlinear filters. Methods. A probabilistic approach to modeling peak counts is presented. First, we sample halos from a mass function. Second, we assign them density profiles. Third, we place those halos randomly on the field of view. The creation of these "fast simulations" requires much less computing time than do N-body runs. Then, we perform ray-tracing through these fast simulation boxes and select peaks from weak-lensing maps to predict peak number counts. The computation is achieved by our C algorithm. Results. We compare our results to N-body simulations to validate our model. We find that our approach is in good agreement with full N-body runs. We show that the lensing signal dominates shape noise and Poisson noise for peaks with S/N between 4 and 6. Also, counts from the same S/N range are sensitive to Ω m and σ 8 . We show how our model can distinguish between various combinations of those two parameters. Conclusions. In this paper, we offer a powerful tool for studying weak-lensing peaks. The potential of our forward model is its high flexibility, which makes the using peak counts under realistic survey conditions feasible.

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Section: Summary and Perspectivesmentioning

confidence: 99%

A new model to predict weak-lensing peak counts

Lin

Kilbinger

2015

A&A

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“…The aperture mass peak analysis, also referred to as the shear peak analysis, is to study peaks in the aperture mass M ap field constructed from the tangential shear component with respect to the point of interest with a filtering function Q (e.g., Schneider et al 1998;Marian et al 2012;Bard et al 2013). Theoretically, M ap corresponds to the convergence field filtered with a compensated window function U where U and Q are related.…”

Section: Theoretical Aspectsmentioning

confidence: 99%

“…To overcome this limitation, higher order cosmic shear correlation analyses are a natural extension (e.g., Semboloni et al 2011;van Waerbeke et al 2013;Fu et al 2014). Weak lensing peak statistics, i.e., concentrating on high signal regions, is another way to probe efficiently the nonlinear regime of the structure formation, and thus can provide important complements to the cosmic shear 2-pt correlation analysis (e.g., White et al 2002;Hamana et al 2004;Tang & Fan 2005;Hennawi & Spergel 2005;Dietrich & Hartlap 2010;Kratochvil et al 2010;Yang et al 2011;Marian et al 2012;Hilbert et al 2012;Bard et al 2013;Lin & Kilbinger 2015) Observationally, different analyses have proved the feasibility of performing weak lensing peak searches from data (e.g., Wittman et al 2006;Gavazzi & Soucail 2007;Miyazaki et al 2007;Geller et al 2010). However, up to now, few cosmological constraints are derived from weak lensing peak statistics in real observations.…”

Section: Introductionmentioning

confidence: 99%

Cosmological constraints from weak lensing peak statistics with Canada-France-Hawaii Telescope Stripe 82 Survey

Liu¹,

Pan²,

Li³

et al. 2015

Monthly Notices of the Royal Astronomical Society

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We derived constraints on cosmological parameters using weak lensing peak statistics measured on the ∼ 130 deg 2 of the Canada-France-Hawaii Telescope Stripe 82 Survey (CS82). This analysis demonstrates the feasibility of using peak statistics in cosmological studies. For our measurements, we considered peaks with signal-to-noise ratio in the range of ν = [3, 6]. For a flat ΛCDM model with only (Ω m , σ 8 ) as free parameters, we constrained the parameters of the following relation Σ 8 = σ 8 (Ω m /0.27) α to be: Σ 8 = 0.82 ± 0.03 and α = 0.43 ± 0.02. The α value found is considerably smaller than the one measured in two-point and three-point cosmic shear correlation analyses, showing a significant complement of peak statistics to standard weak lensing cosmological studies. The derived constraints on (Ω m , σ 8 ) are fully consistent with the ones from either WMAP9 or Planck. From the weak lensing peak abundances alone, we obtained marginalised mean values of Ω m = 0.38 +0.27 −0.24 and σ 8 = 0.81 ± 0.26. Finally, we also explored the potential of using weak lensing peak statistics to constrain the mass-concentration relation of dark matter halos simultaneously with cosmological parameters.

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“…In an era where cosmology is data driven, accurate numerical simulations of shear fields are becoming important for several reasons, including assessing baryonic effects [9][10][11][12][13][14], the utility of non-Gaussian statistics [15][16][17][18][19][20][21][22][23] and various systematic effects [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Sample variance in weak lensing: How many simulations are required?

2016

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Constraining cosmology using weak gravitational lensing consists of comparing a measured feature vector of dimension N b with its simulated counterpart. An accurate estimate of the N b × N b feature covariance matrix C is essential to obtain accurate parameter confidence intervals. When C is measured from a set of simulations, an important question is how large this set should be. To answer this question, we construct different ensembles of Nr realizations of the shear field, using a common randomization procedure that recycles the outputs from a smaller number Ns ≤ Nr of independent ray-tracing N -body simulations. We study parameter confidence intervals as a function of (Ns, Nr) in the range 1 ≤ Ns ≤ 200 and 1 ≤ Nr 10 5 . Previous work [1] has shown that Gaussian noise in the feature vectors (from which the covariance is estimated) lead, at quadratic order, to an O(1/Nr) degradation of the parameter confidence intervals. Using a variety of lensing features measured in our simulations, including shear-shear power spectra and peak counts, we show that cubic and quartic covariance fluctuations lead to additional O(1/N 2 r ) error degradation that is not negligible when Nr is only a factor of few larger than N b . We study the large Nr limit, and find that a single, 240Mpc/h sized 512 3 -particle N -body simulation (Ns = 1) can be repeatedly recycled to produce as many as Nr = few × 10 4 shear maps whose power spectra and high-significance peak counts can be treated as statistically independent. As a result, a small number of simulations (Ns = 1 or 2) is sufficient to forecast parameter confidence intervals at percent accuracy.PACS numbers: 95.36.+x, 95.30.Sf, 98.62.Sb

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Effect of Measurement Errors on Predicted Cosmological Constraints From Shear Peak Statistics With Large Synoptic Survey Telescope

Cited by 25 publications

References 57 publications

A new model to predict weak-lensing peak counts

A new model to predict weak-lensing peak counts

Cosmological constraints from weak lensing peak statistics with Canada-France-Hawaii Telescope Stripe 82 Survey

Sample variance in weak lensing: How many simulations are required?

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