Dark Energy Survey Year 3 results: cosmology with moments of weak lensing mass maps – validation on simulations

Gatti, M.; Chang, C.; Friedrich, Oliver; Jain, Bhuvnesh; Bacon, David; Crocce, M.; DeRose, Joseph J.; Ferrero, I.; Fosalba, P.; Gaztañaga, E.; Gruen, D.; Harrison, I.; Jeffrey, Niall; McClintock, Tom; Secco, L. F.; Whiteway, L.; Abbott, T. M. C.; Allam, S.; Annis, J.; Ávila, S.; Buckley-Geer, E.; Burke, D. L.; Rosell, A. Carnero; Kind, M. Carrasco; Carretero, J.; Cawthon, R.; Costa, L. N. da; Vicente, J. De; Desai, S.; Diehl, H. T.; Doel, P.; Eifler, T. F.; Estrada, J.; Everett, S.; Evrard, A. E.; Frieman, Joshua A.; García-Bellido, J.; Gerdes, D. W.; Gruendl, R. A.; Gschwend, J.; Gutiérrez, G.; James, D. J.; Johnson, Michael D.; Krause, E.; Kuêhn, K.; Lima, M.; Maia, M. A. G.; March, M.; Marshall, J. L.; Menanteau, F.; Miquel, R.; Palmese, A.; Paz-Chinchón, F.; Plazas, A. A.; Sánchez, C.; Sánchez, E.; Scarpine, V.; Schubnell, M.; Santiago, Salvador; Sevilla-Noarbe, I.; Smith, M.; Soares-Santos, M.; Suchyta, E.; Swanson, M. E. C.; Tarlè, G.; Thomas, D.; Troxel, M. A.; Zuntz, J.

doi:10.1093/mnras/staa2680

“…The mass map histograms and the peak counts are simple statistics used to compare the maps and constrain cosmological models (see Gatti et al, 2020;Kacprzak et al, 2016 for examples). These metrics, however, ignore the spatial information in the maps.…”

Section: Quantitative Comparison Metricsmentioning

confidence: 99%

Emulation of Cosmological Mass Maps with Conditional Generative Adversarial Networks

Perraudin

¹

,

Marcon

²

,

Lucchi

³

et al. 2021

Front. Artif. Intell.

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Weak gravitational lensing mass maps play a crucial role in understanding the evolution of structures in the Universe and our ability to constrain cosmological models. The prediction of these mass maps is based on expensive N-body simulations, which can create a computational bottleneck for cosmological analyses. Simulation-based emulators of map summary statistics, such as the matter power spectrum and its covariance, are starting to play increasingly important role, as the analytical predictions are expected to reach their precision limits for upcoming experiments. Creating an emulator of the cosmological mass maps themselves, rather than their summary statistics, is a more challenging task. Modern deep generative models, such as Generative Adversarial Networks (GAN), have demonstrated their potential to achieve this goal. Most existing GAN approaches produce simulations for a fixed value of the cosmological parameters, which limits their practical applicability. We propose a novel conditional GAN model that is able to generate mass maps for any pair of matter density Ωm and matter clustering strength σ8, parameters which have the largest impact on the evolution of structures in the Universe, for a given source galaxy redshift distribution n(z). Our results show that our conditional GAN can interpolate efficiently within the space of simulated cosmologies, and generate maps anywhere inside this space with good visual quality high statistical accuracy. We perform an extensive quantitative comparison of the N-body and GAN -generated maps using a range of metrics: the pixel histograms, peak counts, power spectra, bispectra, Minkowski functionals, correlation matrices of the power spectra, the Multi-Scale Structural Similarity Index (MS-SSIM) and our equivalent of the Fréchet Inception Distance. We find a very good agreement on these metrics, with typical differences are <5% at the center of the simulation grid, and slightly worse for cosmologies at the grid edges. The agreement for the bispectrum is slightly worse, on the <20% level. This contribution is a step toward building emulators of mass maps directly, capturing both the cosmological signal and its variability. We make the code1 and the data2 publicly available.

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“…non-Gaussian) summary statistics, and whether we understand, at the same level as the two-point statistics, the non-trivial systematic effects in these higher-order statistics. Common higher-order statistics with weak lensing include shear peak statistics (Dietrich & Hartlap 2010;Kratochvil et al 2010;Liu et al 2015;Kacprzak et al 2016;Martinet et al 2018;Peel et al 2018;Shan et al 2018;Ajani et al 2020), higher moments of the weak lensing convergence (Van Waerbeke et al 2013;Petri et al 2015;Vicinanza et al 2016;Chang et al 2018;Vicinanza et al 2018;Peel et al 2018;Gatti et al 2020b), three-point correlation functions or bispectra (Takada & Jain 2003Semboloni et al 2011;Fu et al 2014), Minkowski functionals (Kratochvil et al 2012;Petri et al 2015;Vicinanza et al 2019;Parroni et al 2020), and machine-learning methods (Ribli et al 2019;Fluri et al 2018Fluri et al , 2019Jeffrey et al 2021). Many of these have recently been applied to data (Liu et al 2015;Kacprzak et al 2016;Martinet et al 2018;Fluri et al 2019;Jeffrey et al 2021), often performing well in terms of cosmological constraints.…”

Section: Introductionmentioning

confidence: 99%

Dark Energy Survey Year 3 results: Curved-sky weak lensing mass map reconstruction

Jeffrey

¹

,

Gatti

²

,

Chang

³

et al. 2021

Monthly Notices of the Royal Astronomical Society

Self Cite

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We present reconstructed convergence maps, 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 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 ΛCDM 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.

show abstract

“…non-Gaussian) summary statistics, and whether we understand, at the same level as the two-point statistics, the non-trivial systematic effects in these higher-order statistics. Common higher-order statistics with weak lensing include shear peak statistics (Dietrich & Hartlap 2010;Kratochvil et al 2010;Liu et al 2015;Kacprzak et al 2016;Martinet et al 2018;Peel et al 2018;Shan et al 2018;Ajani et al 2020), higher moments of the weak lensing convergence (Van Waerbeke et al 2013;Petri et al 2015;Vicinanza et al 2016;Chang et al 2018;Vicinanza et al 2018;Peel et al 2018;Gatti et al 2020b), three-point correlation functions or bispectra (Takada & Jain 2003Semboloni et al 2011;Fu et al 2014), Minkowski functionals (Kratochvil et al 2012;Petri et al 2015;Vicinanza et al 2019;Parroni et al 2020), and machine-learning methods (Ribli et al 2019;Fluri et al 2018Fluri et al , 2019Jeffrey et al 2021). Many of these have recently been applied to data (Liu et al 2015;Kacprzak et al 2016;Martinet et al 2018;Fluri et al 2019;Jeffrey et al 2021), often performing well in terms of cosmological constraints.…”

Section: Introductionmentioning

confidence: 99%

Dark Energy Survey Year 3 results: curved-sky weak lensing mass map reconstruction

Jeffrey,

Gatti,

Chang

et al. 2021

Preprint

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We present reconstructed convergence maps, 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 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 ΛCDM 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.

show abstract

Dark Energy Survey Year 3 results: cosmology with moments of weak lensing mass maps – validation on simulations

Cited by 44 publications

References 158 publications

Emulation of Cosmological Mass Maps with Conditional Generative Adversarial Networks

Emulation of Cosmological Mass Maps with Conditional Generative Adversarial Networks

Dark Energy Survey Year 3 results: Curved-sky weak lensing mass map reconstruction

Dark Energy Survey Year 3 results: curved-sky weak lensing mass map reconstruction

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