New stochastic approach to cumulative weak lensing

Kainulainen, Kimmo; Marra, Valerio

doi:10.1103/physrevd.80.123020

Cited by 58 publications

(87 citation statements)

References 60 publications

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“…Previous work has shown that correlated structures influence number and height of peaks by only a few percentage points (Marian et al 2010). Furthermore, assuming a stochastical distribution of halos can lead to accurate predictions of the convergence probability distirbution function (Kainulainen & Marra 2009). One may also notice that halos can overlap in 3D space, and indeed we do not exclude this possibility.…”

Section: Probabilistic Approach: Fast Simulationsmentioning

confidence: 81%

“…To validate this method and to justify various hypotheses that our model makes, we compare results from our fast simulations to those from N-body runs. This approach is similar to the sGL model of Kainulainen & Marra (2009, 2011a, where they show that the stochastic process provides a quick and accurate way to recover the lensing signal distribution.…”

Section: Introductionmentioning

confidence: 99%

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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: Probabilistic Approach: Fast Simulationsmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

A new model to predict weak-lensing peak counts

Lin

Kilbinger

2015

A&A

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“…The second family consists of analytic predictions (Maturi et al 2010;Fan et al 2010) based on Gaussian random field theory. A third approach has been introduced by Lin & Kilbinger (2015, hereafter Paper I): similar to Kruse & Schneider (1999) and Kainulainen & Marra (2009, 2011a, we propose a stochastic process to predict peak counts by simulating lensing maps from a halo distribution drawn from the mass function.…”

Section: Introductionmentioning

confidence: 99%

A new model to predict weak-lensing peak counts

Lin

Kilbinger

2015

A&A

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Context. Peak counts have been shown to be an excellent tool for extracting the non-Gaussian part of the weak lensing signal. Recently, we developed a fast stochastic forward model to predict weak-lensing peak counts. Our model is able to reconstruct the underlying distribution of observables for analysis. Aims. In this work, we explore and compare various strategies for constraining a parameter using our model, focusing on the matter density Ω m and the density fluctuation amplitude σ 8 . Methods. First, we examine the impact from the cosmological dependency of covariances (CDC). Second, we perform the analysis with the copula likelihood, a technique that makes a weaker assumption than does the Gaussian likelihood. Third, direct, non-analytic parameter estimations are applied using the full information of the distribution. Fourth, we obtain constraints with approximate Bayesian computation (ABC), an efficient, robust, and likelihood-free algorithm based on accept-reject sampling. Results. We find that neglecting the CDC effect enlarges parameter contours by 22% and that the covariance-varying copula likelihood is a very good approximation to the true likelihood. The direct techniques work well in spite of noisier contours. Concerning ABC, the iterative process converges quickly to a posterior distribution that is in excellent agreement with results from our other analyses. The time cost for ABC is reduced by two orders of magnitude. Conclusions. The stochastic nature of our weak-lensing peak count model allows us to use various techniques that approach the true underlying probability distribution of observables, without making simplifying assumptions. Our work can be generalized to other observables where forward simulations provide samples of the underlying distribution.

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“…Finally, one may insert the expressions obtained for A 1 (t) and A 2 (t) into the expression (17) for the exterior pressure p e . Moreover, demanding that the pressure is continuous across the boundary allows one also to derive a form for p b (t).…”

Section: Exterior Regionmentioning

confidence: 99%

“…By observing a source in the exterior region, one would then measure an apparent acceleration of the universe's expansion, but this would be only a local effect. The effects of local inhomogeneities on the apparent acceleration of the universe have been widely studied [13][14][15][16][17], and have been linked with the observations of distant Type-IA supernova. We anticipate that our model may also be useful in such a context, although we leave the investigation of this to future research.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of a spherical object of uniform density in an expanding universe

Nandra¹,

Lasenby²,

Hobson³

2013

Phys. Rev. D

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We present Newtonian and fully general-relativistic solutions for the evolution of a spherical region of uniform interior density ρi(t), embedded in a background of uniform exterior density ρe(t). In both regions, the fluid is assumed to support pressure. In general, the expansion rates of the two regions, expressed in terms of interior and exterior Hubble parameters Hi(t) and He(t), respectively, are independent. We consider in detail two special cases: an object with a static boundary, Hi(t) = 0; and an object whose internal Hubble parameter matches that of the background, Hi(t) = He(t).In the latter case, we also obtain fully general-relativistic expressions for the force required to keep a test particle at rest inside the object, and that required to keep a test particle on the moving boundary. We also derive a generalised form of the Oppenheimer-Volkov equation, valid for general time-dependent spherically-symmetric systems, which may be of interest in its own right.

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New stochastic approach to cumulative weak lensing

Cited by 58 publications

References 60 publications

A new model to predict weak-lensing peak counts

A new model to predict weak-lensing peak counts

A new model to predict weak-lensing peak counts

Dynamics of a spherical object of uniform density in an expanding universe

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