Lossless, Scalable Implicit Likelihood Inference for Cosmological Fields

L. Makinen, T. Charnock, J. Alsing, and B. Wandelt

We present a comparison of simulation-based inference to full, field-based analytical inference in cosmological data analysis. We demonstrate speed and optimality of summaries computed using Information Maximizing Neural Networks. A single iteration of IMNN training enhances robustness to the choice of fiducial model.

CARPool Covariance: Fast, unbiased covariance estimation for large-scale structure observables

Nicolas Chartier and Benjamin Wandelt

We develop a matrix generalization of Convergence Acceleration by Regression and Pooling (CARPool) to combine a small number of expensive simulations with fast surrogates and obtain low-noise estimates of the covariance matrix that are unbiased by construction.

The GIGANTES dataset: precision cosmology from voids in the machine learning era

C. Kreisch et al.

We present GIGANTES, the most extensive and realistic void catalog suite ever released -- containing over 1 billion cosmic voids covering a volume larger than the observable Universe, more than 20 TB of data,...

Solving high-dimensional parameter inference: marginal posterior densities and Moment Networks

Niall Jeffrey, Benjamin Wandelt

We propose direct estimation of lower-dimensional marginal distributions, bypassing high-dimensional density estimation or high-dimensional MCMC.. By evaluating the two-dimensional marginal posteriors we can unveil the full-dimensional parameter covariance structure. In addition, we construct a simple hierarchy of fast neural regression models, called Moment Networks, that compute increasing moments of any desired lower-dimensional marginal posterior density.

CARPool: fast, accurate computation of large-scale structure statistics by pairing costly and cheap cosmological simulations

Chartier, Nicolas; Wandelt, Benjamin; Akrami, Yashar; Villaescusa-Navarro, Francisco

We propose a general method that exploits the correlation between simulations and surrogates to compute fast, reduced-variance statistics of large-scale structure observables without model error at the cost of only a few simulations. We call this approach Convergence Acceleration by Regression and Pooling (CARPool).

The Quijote Simulations

Villaescusa-Navarro, Francisco; Hahn, ChangHoon; Massara, Elena and 26 more

The QUIJOTE simulations are a set of 44,100 full N-body simulations spanning more than 7000 cosmological models in the {Ωm,Ωb,h,ns,σ8,Mν,w} hyperplane.

Cosmology Intertwined IV: The Age of the Universe and its Curvature

Di Valentino, Eleonora; Anchordoqui, Luis A.; Ali-Haimoud, Yacine and 75 more

A precise measurement of the curvature of the Universe is of primeval importance for cosmology since it could not only confirm the paradigm of primordial inflation but also help in discriminating between different early Universe scenarios.

Cosmology Intertwined III: fσ8 and S8

Di Valentino, Eleonora; Anchordoqui, Luis A.; Ali-Haimoud, Yacine and 75 more

The standard Λ Cold Dark Matter cosmological model provides a wonderful fit to current cosmological data, but a few tensions and anomalies became statistically significant with the latest data analyses.

Cosmology Intertwined II: The Hubble Constant Tension

Di Valentino, Eleonora; Anchordoqui, Luis A.; Ali-Haimoud, Yacine and 75 more

The current cosmological probes have provided a fantastic confirmation of the standard Λ Cold Dark Matter cosmological model, that has been constrained with unprecedented accuracy. However, with the increase of the experimental sensitivity a few statistically significant tensions between different independent cosmological datasets emerged.

Cosmology Intertwined I: Perspectives for the Next Decade

Di Valentino, Eleonora; Anchordoqui, Luis A.; Ali-Haimoud, Yacine and 75 more

The standard Λ Cold Dark Matter cosmological model provides an amazing description of a wide range of astrophysical and astronomical data. However, there are a few big open questions, that make the standard model look like a first-order approximation to a more realistic scenario that still needs to be fully understood.

Precision cosmology with voids in the final BOSS data

Hamaus, Nico; Pisani, Alice; Choi, Jin-Ah; Lavaux, Guilhem; Wandelt, Benjamin D.; Weller, Jochen

We report novel cosmological constraints obtained from cosmic voids in the final BOSS DR12 dataset. They arise from the joint analysis of geometric and dynamic distortions of average void shapes in redshift space.

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