Do dark matter halos explain lensing peaks?

Matilla, José Manuel Zorrilla; Haiman, Zoltán; Hsu, Daniel; Gupta, Arushi; Petri, Andrea

doi:10.1103/physrevd.94.083506

Cited by 27 publications

(25 citation statements)

References 41 publications

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“…If the synergy between WL and other probes is to be optimized to lift degeneracy, Σ 8 could be misleading. We also point out that Zorrilla Matilla et al (2016) have found that the peak count covariance of Camelus is underestimated. This means that the contours in the left panel of Fig.…”

Section: Comparison With ξ ±mentioning

confidence: 99%

“…Excluding the high-bias bins also weakens the constraining power, thereby broadening the contours. Whereas Zorrilla Matilla et al (2016) have found a good agreement between the Camelus algorithm and N-body runs on small fields, this test suggests that the systematics of Camelus need to be carefully studied in order to achieve accurate results for large-field analyses.…”

Section: Parameter Constraints From Peak Countsmentioning

confidence: 99%

“…Zorrilla Matilla et al (2016) have already identified some concerning the fast halo modelling. These include the lack of halo clustering, inaccurate modelling of the halo concentration, and the use of NFW profiles.…”

Section: Parameter Constraints From Peak Countsmentioning

confidence: 99%

“…We expect contributing systematics to include the lack of halo clustering, inaccurate modelling of the halo concentration, and the limitations of NFW profiles. It has recently been shown that these are a minor issue for relatively small fields (Zorrilla Matilla et al 2016), but the assumptions of Camelus clearly break down for large-area surveys.…”

Section: +006mentioning

confidence: 99%

“…Camelus was introduced in Lin & Kilbinger (2015a) and studied further in the context of parameter constraint strategies and mass-map filtering methods in Lin & Kilbinger (2015b) and Lin et al (2016), respectively. See also Zorrilla Matilla et al (2016) for a recent comparison of Camelus predictions to N-body simulations over a broad range of cosmologies. The code uses a forward-model approach to generate lensing catalogues in a way that avoids time-costly N-body simulations and only requires a halo mass function and halo profile as input.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Comparison With ξ ±mentioning

confidence: 99%

Section: Parameter Constraints From Peak Countsmentioning

confidence: 99%

Section: Parameter Constraints From Peak Countsmentioning

confidence: 99%

Section: +006mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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An improved cosmological parameter inference scheme motivated by deep learning

2018

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Dark matter cannot be observed directly, but its weak gravitational lensing slightly distorts the apparent shapes of background galaxies, making weak lensing one of the most promising probes of cosmology. Several observational studies have measured the effect, and there are currently running [1,2], and planned efforts [3,4] to provide even larger, and higher resolution weak lensing maps. Due to nonlinearities on small scales, the traditional analysis with two-point statistics does not fully capture all the underlying information [5]. Multiple inference methods were proposed to extract more details based on higher order statistics [6, 7], peak statistics [8][9][10][11][12][13], Minkowski functionals [14-16] and recently convolutional neural networks (CNN) [17,18]. Here we present an improved convolutional neural network that gives significantly better estimates of Ω m and σ 8 cosmological parameters from simulated convergence maps than the state of art methods and also is free of systematic bias. We show that the network exploits information in the gradients around peaks, and with this insight, we construct a new, easy-to-understand, and robust peak counting algorithm based on the 'steepness' of peaks, instead of their heights. The proposed scheme is even more accurate than the neural network on high-resolution noiseless maps. With shape noise and lower resolution its relative advantage deteriorates, but it remains more accurate than peak counting.Following the idea and using the simulation data from a recent study [18] we created an improved convolutional neural network (CNN) architecture (see details in the Methods) which is able to recover cosmological parameters more accurately from simulated weak lensing maps. The input of the network is a set of mock convergence (κ) maps generated by ray-tracing n-body simulations with 96 different values for the matter density Ω m and the scale of the initial perturbations normalized at the late Universe, σ 8 (see [18] and [19] for details of the weak lensing map generation), the outputs of the network were the predicted cosmological parameters. The modifications of the CNN mostly consisted of adding further activations, increasing the number of filters, and introducing a regular block structure, following successful computer vision models [20,21].

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Probing dark energy with tomographic weak-lensing aperture mass statistics

Martinet

Harnois-Déraps

Jullo

et al. 2021

A&A

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We forecast and optimize the cosmological power of various weak-lensing aperture mass (Map) map statistics for future cosmic shear surveys, including peaks, voids, and the full distribution of pixels (1D Map). These alternative methods probe the non-Gaussian regime of the matter distribution, adding complementary cosmological information to the classical two-point estimators. Based on the SLICS and cosmo-SLICS N-body simulations, we build Euclid-like mocks to explore the S8 − Ωm − w0 parameter space. We develop a new tomographic formalism that exploits the cross-information between redshift slices (cross-Map) in addition to the information from individual slices (auto-Map) probed in the standard approach. Our auto-Map forecast precision is in good agreement with the recent literature on weak-lensing peak statistics and is improved by ∼50% when including cross-Map. It is further boosted by the use of 1D Map that outperforms all other estimators, including the shear two-point correlation function (γ-2PCF). When considering all tomographic terms, our uncertainty range on the structure growth parameter S8 is enhanced by ∼45% (almost twice better) when combining 1D Map and the γ-2PCF compared to the γ-2PCF alone. We additionally measure the first combined forecasts on the dark energy equation of state w0, finding a factor of three reduction in the statistical error compared to the γ-2PCF alone. This demonstrates that the complementary cosmological information explored by non-Gaussian Map map statistics not only offers the potential to improve the constraints on the recent σ8–Ωm tension, but also constitutes an avenue to understanding the accelerated expansion of our Universe.

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Do dark matter halos explain lensing peaks?

Cited by 27 publications

References 41 publications

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

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

An improved cosmological parameter inference scheme motivated by deep learning

Probing dark energy with tomographic weak-lensing aperture mass statistics

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