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Register a new userWeak lensing scattering transform: dark energy and neutrino mass sensitivity
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As weak lensing surveys become deeper, they reveal more non-Gaussian aspects of the convergence field which can only be extracted using statistics beyond the power spectrum. In Cheng et al. (2020) we showed that the scattering transform, a novel statistic borrowing mathematical concepts from convolutional neural networks, is a powerful tool for cosmological parameter estimation in the non-Gaussian regime. Here, we extend that analysis to explore its sensitivity to dark energy and neutrino mass parameters with weak lensing surveys. We first use image synthesis to show visually that, compared to the power spectrum and bispectrum, the scattering transform provides a better statistical vocabulary to characterize the perceptual properties of lensing mass maps. We then show that it is also better suited for parameter inference: (i) it provides higher sensitivity in the noiseless regime, and (ii) at the noise level of Rubin-like surveys, though the constraints are not significantly tighter than those of the bispectrum, the scattering coefficients have a more Gaussian sampling distribution, which is an important property for likelihood parametrization and accurate cosmological inference. We argue that the scattering coefficients are preferred statistics considering both constraining power and likelihood properties.
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Weak gravitational lensing provides a sensitive probe of cosmology by measuring the mass distribution and the geometry of the low redshift universe. We show how an all-sky weak lensing tomographic survey can jointly constrain different sets of cosmological parameters describing dark energy, massive neutrinos (hot dark matter), and the primordial power spectrum. In order to put all sectors on an equal footing, we introduce a new parameter $beta$, the second order running spectral index. Using the Fisher matrix formalism with and without CMB priors, we examine how the constraints vary as the parameter set is enlarged. We find that weak lensing with CMB priors provides robust constraints on dark energy parameters and can simultaneously provide strong constraints on all three sectors. We find that the dark energy sector is largely insensitive to the inclusion of the other cosmological sectors. Implications for the planning of future surveys are discussed.
We forecast and optimize the cosmological power of various weak-lensing aperture mass ($M_{rm ap}$) map statistics for future cosmic shear surveys, including peaks, voids, and the full distribution of pixels (1D $M_{rm ap}$). 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 $S_8 - Omega_{rm m} - w_0$ parameter space. We develop a new tomographic formalism which exploits the cross-information between redshift slices (cross-$M_{rm ap}$) in addition to the information from individual slices (auto-$M_{rm ap}$) probed in the standard approach. Our auto-$M_{rm ap}$ forecast precision is in good agreement with the recent literature on weak-lensing peak statistics, and is improved by $sim 50$% when including cross-$M_{rm ap}$. It is further boosted by the use of 1D $M_{rm ap}$ that outperforms all other estimators, including the shear two-point correlation function ($gamma$-2PCF). When considering all tomographic terms, our uncertainty range on the structure growth parameter $S_8$ is enhanced by $sim 45$% (almost twice better) when combining 1D $M_{rm ap}$ and the $gamma$-2PCF compared to the $gamma$-2PCF alone. We additionally measure the first combined forecasts on the dark energy equation of state $w_0$, finding a factor of three reduction of the statistical error compared to the $gamma$-2PCF alone. This demonstrates that the complementary cosmological information explored by non-Gaussian $M_{rm ap}$ map statistics not only offers the potential to improve the constraints on the recent $sigma_8$ - $Omega_{rm m}$ tension, but also constitutes an avenue to understand the accelerated expansion of our Universe.
We construct the largest curved-sky galaxy weak lensing mass map to date from the DES first-year (DES Y1) data. The map, about 10 times larger than previous work, is constructed over a contiguous $approx1,500 $deg$^2$, covering a comoving volume of $approx10 $Gpc$^3$. The effects of masking, sampling, and noise are tested using simulations. We generate weak lensing maps from two DES Y1 shear catalogs, Metacalibration and Im3shape, with sources at redshift $0.2<z<1.3,$ and in each of four bins in this range. In the highest signal-to-noise map, the ratio between the mean signal-to-noise in the E-mode and the B-mode map is $sim$1.5 ($sim$2) when smoothed with a Gaussian filter of $sigma_{G}=30$ (80) arcminutes. The second and third moments of the convergence $kappa$ in the maps are in agreement with simulations. We also find no significant correlation of $kappa$ with maps of potential systematic contaminants. Finally, we demonstrate two applications of the mass maps: (1) cross-correlation with different foreground tracers of mass and (2) exploration of the largest peaks and voids in the maps.
We introduce a novel approach to reconstruct dark matter mass maps from weak gravitational lensing measurements. The cornerstone of the proposed method lies in a new modelling of the matter density field in the Universe as a mixture of two components:(1) a sparsity-based component that captures the non-Gaussian structure of the field, such as peaks or halos at different spatial scales; and (2) a Gaussian random field, which is known to well represent the linear characteristics of the field.Methods. We propose an algorithm called MCALens which jointly estimates these two components. MCAlens is based on an alternating minimization incorporating both sparse recovery and a proximal iterative Wiener filtering. Experimental results on simulated data show that the proposed method exhibits improved estimation accuracy compared to state-of-the-art mass map reconstruction methods.
We present reconstructed convergence maps, textit{mass maps}, from the Dark Energy Survey (DES) third year (Y3) weak gravitational lensing data set. The mass maps are weighted projections of the density field (primarily dark matter) in the foreground of the observed galaxies. We use four reconstruction methods, each is a textit{maximum a posteriori} estimate with a different model for the prior probability of the map: Kaiser-Squires, null B-mode prior, Gaussian prior, and a sparsity prior. All methods are implemented on the celestial sphere to accommodate the large sky coverage of the DES Y3 data. We compare the methods using realistic $Lambda$CDM simulations with mock data that are closely matched to the DES Y3 data. We quantify the performance of the methods at the map level and then apply the reconstruction methods to the DES Y3 data, performing tests for systematic error effects. The maps are compared with optical foreground cosmic-web structures and are used to evaluate the lensing signal from cosmic-void profiles. The recovered dark matter map covers the largest sky fraction of any galaxy weak lensing map to date.
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