<tt>KaRMMa</tt>– kappa reconstruction for mass mapping

Fiedorowicz, Pier; Rozo, Eduardo; Boruah, Supranta S.; Chang, C.; Gatti, M.

doi:10.1093/mnras/stac468

Cited by 13 publications

(14 citation statements)

References 74 publications

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“…Figure 6 formalizes our visual impression, demonstrating that including the cross-correlations in the mass mapping procedure results in substantially improved cross-correlations between the sam-7 For a comparison to simulations and further discussion, see Fiedorowicz et al (2022). 8 A notable exception is the mass mapping method of Alsing et al (2016Alsing et al ( , 2017 pled maps and the true maps.…”

Section: Tomographic Mass Map Reconstructionmentioning

confidence: 56%

“…Our method reconstructs the mass maps assuming that the convergence field can be approximated as a multivariate lognormal distribution, thus recovering the correct one point and two point statistics, as well as peak and void counts, as measured in simulations (Fiedorowicz et al 2022). Our method also consistently accounts for cross correlations between different tomographic bins.…”

Section: Discussionmentioning

confidence: 97%

“…We will extend the code to work on the curved sky in a future work. The first steps towards a curved sky implementation of mass mapping using a lognormal prior were already presented in Fiedorowicz et al (2022). Furthermore, we performed our inference assuming no systematic effects are present.…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, we extend the framework of the KaRMMa algorithm we developed in Fiedorowicz et al (2022) to enable map-based inference of cosmological parameters. The KaRMMa algorithm samples the real-space values of the convergence field subject to a log-normal prior and conditioned on cosmic shear data.…”

Section: Introductionmentioning

confidence: 99%

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Map-based cosmology inference with lognormal cosmic shear maps

Boruah¹,

Rozo²,

Fiedorowicz³

2022

Preprint

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Most cosmic shear analyses to date have relied on summary statistics (e.g. 𝜉 + and 𝜉 − ). These types of analyses are necessarily sub-optimal, as the use of summary statistics is lossy. In this paper, we forward-model the convergence field of the Universe as a lognormal random field conditioned on the observed shear data. This new map-based inference framework enables us to recover the joint posterior of the cosmological parameters and the convergence field of the Universe. Our analysis properly accounts for the covariance in the mass maps across tomographic bins, which significantly improves the fidelity of the maps relative to single-bin reconstructions. We verify that applying our inference pipeline to Gaussian random fields recovers posteriors that are in excellent agreement with their analytical counterparts. At the resolution of our maps -and to the extent that the convergence field can be described by the lognormal model -our map posteriors allow us to reconstruct all summary statistics (including non-Gaussian statistics). We forecast that a map-based inference analysis of LSST-Y10 data can improve cosmological constraints in the 𝜎 8 -Ω m plane by ≈ 30% relative to the currently standard cosmic shear analysis. This improvement happens almost entirely along the 𝑆 8 = 𝜎 8 Ω 1/2 m directions, meaning map-based inference fails to significantly improve constraints on 𝑆 8 .

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Section: Tomographic Mass Map Reconstructionmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Map-based cosmology inference with lognormal cosmic shear maps

Boruah¹,

Rozo²,

Fiedorowicz³

2022

Preprint

Self Cite

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“…More recently, Porqueres et al (2021) extended this work and demonstrated mass-map posterior sampling with a non-linear prior defined by a forward gravity model, but limited to very low angular resolution. Other Bayesian posterior sampling approaches include Schneider et al (2016) which relied on a Gaussian Process prior, which proposed a proximal MCMC approach to accommodate non-differentiable sparsity priors, and Fiedorowicz et al (2021) which relies on a log-normal prior.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Mass Mapping with Neural Score Estimation

Rémy¹,

Lanusse²,

Jeffrey³

et al. 2022

Preprint

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Context. Weak lensing mass-mapping is a useful tool to access the full distribution of dark matter on the sky, but because of intrinsic galaxy ellipticies and finite fields/missing data, the recovery of dark matter maps constitutes a challenging ill-posed inverse problem. Aims. We introduce a novel methodology allowing for efficient sampling of the high-dimensional Bayesian posterior of the weak lensing mass-mapping problem, and relying on simulations for defining a fully non-Gaussian prior. We aim to demonstrate the accuracy of the method on simulations, and then proceed to applying it to the mass reconstruction of the HST/ACS COSMOS field. Methods. The proposed methodology combines elements of Bayesian statistics, analytic theory, and a recent class of Deep Generative Models based on Neural Score Matching. This approach allows us to do the following: 1) Make full use of analytic cosmological theory to constrain the 2pt statistics of the solution. 2) Learn from cosmological simulations any differences between this analytic prior and full simulations. 3) Obtain samples from the full Bayesian posterior of the problem for robust Uncertainty Quantification. Results. We demonstrate the method on the κTNG simulations (Osato et al. 2021) and find that the posterior mean significantly outperfoms previous methods (Kaiser-Squires, Wiener filter, Sparsity priors) both on root-mean-square error and in terms of the Pearson correlation. We further illustrate the interpretability of the recovered posterior by establishing a close correlation between posterior convergence values and SNR of clusters artificially introduced into a field. Finally, we apply the method to the reconstruction of the HST/ACS COSMOS field and yield the highest quality convergence map of this field to date. Conclusions. We find the proposed approach to be superior to previous algorithms, scalable, providing uncertainties, and using a fully non-Gaussian prior. All codes and data products associated with this paper are available at this link

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Probabilistic mass-mapping with neural score estimation

Remy

Lanusse

Jeffrey

et al. 2023

A&A

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Context. Weak lensing mass-mapping is a useful tool for accessing the full distribution of dark matter on the sky, but because of intrinsic galaxy ellipticies, finite fields, and missing data, the recovery of dark matter maps constitutes a challenging, ill-posed inverse problem Aims. We introduce a novel methodology that enables the efficient sampling of the high-dimensional Bayesian posterior of the weak lensing mass-mapping problem, relying on simulations to define a fully non-Gaussian prior. We aim to demonstrate the accuracy of the method to simulated fields, and then proceed to apply it to the mass reconstruction of the HST/ACS COSMOS field. Methods. The proposed methodology combines elements of Bayesian statistics, analytic theory, and a recent class of deep generative models based on neural score matching. This approach allows us to make full use of analytic cosmological theory to constrain the 2pt statistics of the solution, to understand any differences between this analytic prior and full simulations from cosmological simulations, and to obtain samples from the full Bayesian posterior of the problem for robust uncertainty quantification. Results. We demonstrate the method in the κTNG simulations and find that the posterior mean significantly outperfoms previous methods (Kaiser-Squires, Wiener filter, Sparsity priors) both for the root-mean-square error and in terms of the Pearson correlation. We further illustrate the interpretability of the recovered posterior by establishing a close correlation between posterior convergence values and the S/N of the clusters artificially introduced into a field. Finally, we apply the method to the reconstruction of the HST/ACS COSMOS field, which yields the highest-quality convergence map of this field to date. Conclusions. We find the proposed approach to be superior to previous algorithms, scalable, providing uncertainties, and using a fully non-Gaussian prior.

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`KaRMMa`– kappa reconstruction for mass mapping

Cited by 13 publications

References 74 publications

Map-based cosmology inference with lognormal cosmic shear maps

Map-based cosmology inference with lognormal cosmic shear maps

Probabilistic Mass Mapping with Neural Score Estimation

Probabilistic mass-mapping with neural score estimation

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KaRMMa– kappa reconstruction for mass mapping

Cited by 13 publications

References 74 publications

Map-based cosmology inference with lognormal cosmic shear maps

Map-based cosmology inference with lognormal cosmic shear maps

Probabilistic Mass Mapping with Neural Score Estimation

Probabilistic mass-mapping with neural score estimation

Contact Info

Product

Resources

About

`KaRMMa`– kappa reconstruction for mass mapping