Bayesian Neural Networks improve precision cosmology from simulations.
problem Extracting precise cosmological parameters from complex simulations.
method Using Bayesian Neural Networks on The Quijote simulations.
result Demonstrates BNNs' ability to estimate associated uncertainties and complex output distributions.
Study shows current simulations are insufficient for optimal neural network training in cosmology.
problem Insufficient training data for neural networks in cosmological inference.
method Empirical neural scaling law and Cramer-Rao bound to forecast training simulations needed.
result Current simulation suites do not provide sufficient training data for optimal neural network performance.
LSBI approximates likelihood with linear functions for cosmological parameter estimation.
problem Estimating cosmological parameters from complex data.
method Sequential Linear Simulation-based Inference (LSBI) using Gaussian approximations.
result LSBI achieves convergence after 4-5 rounds of simulations, comparable to neural methods.
CHARM creates mock halo catalogs from dark matter density fields using neural networks.
problem Creating accurate mock halo catalogs for cosmological studies is computationally expensive.
method CHARM uses multi-stage neural spline flow networks to learn the mapping from dark matter density fields to halo catalogs.
result Mock halo catalogs have the same statistical properties as those from high-resolution N-body simulations.
TRIM improves interpretability of deep neural networks in cosmology.
problem Understanding which features a deep neural network uses in a transformed space.
method TRIM (Transformation IMportance) attributes importances to features in a transformed space.
result Combining TRIM with contextual decomposition helps identify physical features learned by DNNs.
A grand challenge of the 21st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe. Galaxy surveys provide the means to map out cosmic large-scale structure in thr…
New method extracts cosmological information from dark matter halo catalogues using graph neural networks.
problem Quantifying cosmological information from large-scale structure data.
method Implicit likelihood approach with Information Maximising Neural Networks (IMNNs) on graph representations of dark matter halo catalogues.
result Graph neural network summaries can extract information from noisy catalogues and improve parameter constraints.
Method trains emulators to estimate posterior probabilities safely.
problem Uncertainty in slow forward model calculations.
method Trains emulators while estimating posterior probabilities with MCMC, propagating error.
result Demonstrates robust posterior inference for ΛCDM cosmology model. Physics-informed neural networks improve baryonic predictions from dark matter simulations.
problem Recreating hydrodynamic simulations from dark matter requires expensive and time-consuming computations.
method Combining neural network architectures with physical constraints and using Kullback-Leibler divergence for prediction comparison.
result Improved accuracy of baryonic predictions based on dark matter halo properties, successful recovery of the metallicity relation, and preserved scatter.
Simplifies inference for simulators with or without tractable likelihoods.
problem Inference for models with intractable likelihoods.
method Amortized simulation-based frequentist inference.
result Valid confidence sets for parameter inference.
Approximate Bayesian Computation (ABC) is a method to obtain a posterior distribution without a likelihood function, using simulations and a set of distance metrics. For that reason, it has recently been gaining popularity as an analysis tool in cosmology and astrophysics. Its drawback, however, is a slow convergence r…
ConfEviSurrogate improves surrogate model accuracy and uncertainty quantification.
problem Uncertainty in surrogate models hinders reliable analysis.
method Introduces ConfEviSurrogate, a novel model that learns evidential distributions, separates uncertainty sources, and provides reliable prediction intervals.
result Demonstrates accurate predictions and robust uncertainty estimates in various simulations.
Bayesian neural networks improve simulation-based inference with limited data.
problem Inaccurate inference in data-poor regimes with limited or expensive simulations.
method Bayesian neural networks for posterior approximation, accounting for computational uncertainty.
result Bayesian neural networks produce well-calibrated posteriors with few simulations.
Bayesian neural networks improve cosmic parameter estimation from modified gravity simulations.
problem Estimating cosmological parameters from large-scale structure data with modified gravity.
method Implement Bayesian neural networks (BNNs) with two cases: single BLL and FullB, trained on dark matter only particle mesh N-body simulations. result BNNs yield well-calibrated uncertainty estimates and accurately predict cosmological parameters for Ωm and σ8. Improves inference from sparse data with hybrid summary statistics.
problem Robust simulation-based inference from limited data.
method Augment traditional summary statistics with neural network outputs to maximize mutual information.
result Improves information extraction and makes inference robust in low-data settings.
TopoFisher learns topological summaries by maximizing Fisher information, improving parameter efficiency and inference quality.
problem Simulation-based inference misses key information in low-order statistics, especially for non-Gaussian fields.
method TopoFisher uses a differentiable persistent-homology pipeline that learns topological summaries by maximizing local Gaussian Fisher information.
result TopoFisher recovers much of the available information and outperforms fixed topological vectorizations in weak gravitational lensing.
Hybrid model speeds up galaxy simulations by incorporating baryonic properties.
problem Inaccurate baryonic properties in dark matter-only simulations.
method Combining analytic models and machine learning for faster, more accurate simulations.
result Hybrid model outperforms machine learning alone for some baryonic properties.
A new method using normalizing flows speeds up Bayesian model comparison.
problem Computational challenges in calculating Bayesian evidence for complex models.
method Savage-Dickey density ratio with normalizing flows.
result The method scales to high-dimensional settings and provides consistent Bayes factors.
A new method models galaxies as points in space for better analysis.
problem Limitations of binning and voxelization in galaxy surveys.
method A diffusion-based generative model for galaxy point clouds.
result Demonstrated on dark matter haloes in Quijote simulations.
Weak lensing maps contain information beyond two-point statistics on small scales. Much recent work has tried to extract this information through a range of different observables or via nonlinear transformations of the lensing field. Here we train and apply a 2D convolutional neural network to simulated noiseless lensi…
Paper introduces a new cosmological volume function and its properties.
problem Introducing a new cosmological volume function.
method Introduces and analyzes the cosmological volume function τ_V, showing it's continuously differentiable.
result τ_V leads to a canonical splitting of the metric tensor and a canonical Wick-rotated Riemannian metric.
Machine learning enhances cosmology through new tools and data analysis.
problem Leveraging machine learning in cosmology for better understanding of the universe.
method Development of new computational tools, data analysis techniques, and community building.
result Substantial potential in cosmology remains untapped with machine learning.
Bayesian method finds voids in galaxy surveys with deep neural networks.
problem Finding genuine matter underdensities in sparse galaxy surveys is underconstrained.
method Deep graph neural network evolves 'test particles' to sample from stochastic void definitions.
result Trained model performs well and finds Bayes-optimal void mappings.
Study how past eon's matter affects present eon in Penrose's cyclic cosmology.
problem Determining present eon's matter content from past eon's matter.
method Use Penrose's reciprocity hypothesis to link past and present eons' matter.
result Perfect fluid matter content of past eon influences present eon's matter content.
COCA accelerates N-body simulations by correcting ML errors.
problem Computational expense and limited trustworthiness of ML emulations.
method Hybrid framework combining ML and N-body simulator in an emulated frame of reference. result COCA reduces emulation errors with fewer force evaluations.
New mass definition for negative cosmological constant spacetimes.
problem Defining quasilocal mass for spacetimes with negative cosmological constant.
method Spinorial approach based on previous work for vanishing cosmological constant.
result Non-negative mass, equal to Misner-Sharp mass in spherical symmetry, zero for AdS.
Study of spacetimes in cosmology without symmetry assumptions.
problem Proving timelike incompleteness and properties of time functions.
method Rigorous mathematical proofs and analysis of spacetime properties.
result Timelike incompleteness for specific spacetimes with mean curvature constraints.
Study shows how 3+1D cosmologies can evolve to de Sitter space under certain conditions.
problem Understanding the evolution of 3+1D cosmologies with specific symmetry constraints.
method Mean Curvature Flow methods applied to cosmologies with positive cosmological constant and specific symmetry groups.
result Asymptotically, 3+1D cosmologies evolve to de Sitter space under certain conditions.
New CMC existence result for expanding cosmological spacetimes.
problem Establishing a new constant mean curvature (CMC) existence result for cosmological spacetimes.
method Construction of barriers in the support sense and asymptotic limit of mean curvature flow.
result The existence of a CMC Cauchy surface in expanding cosmological spacetimes.
New cosmological spacetimes without CMC Cauchy surfaces found.
problem Finding CMC Cauchy surfaces in cosmological spacetimes.
method Generalized Bartnik's construction to connected sums of three-manifolds.
result Cosmological spacetimes without CMC Cauchy surfaces for any compact three-manifolds.
Prominent approaches to quantum gravity struggle when it comes to incorporating a positive cosmological constant in their models. Using quantization of a complex SL(2,C) Chern-Simons theory we include a cosmological constant, of either sign, into a model of quantum gravity.
Improved formulation of spinfoam quantum gravity with cosmological constant, ensuring all amplitudes are finite and providing semiclassical asymptotics.
problem Ensuring the finiteness of spinfoam amplitudes and providing semiclassical asymptotics for quantum gravity.
method Using state-integral model of PSL(2, C) Chern-Simons theory and implementing simplicity constraint. result All spinfoam amplitudes are finite and provide semiclassical asymptotics with oscillatory terms related to the Regge action.
Rubin LSST DESC uses AI/ML for dark energy research.
problem Challenges in uncertainty quantification and model robustness for AI/ML in DESC.
method Bayesian inference, physics-informed methods, validation frameworks, active learning.
result AI/ML methods are essential but require rigorous evaluation and governance.
We study Einstein's equation in (m+n)D and (1+n)D warped spaces (Mˉ,gˉ) and classify all such spaces satisfying Einstein equations Gˉ=−Λˉgˉ. We show that the warping function not only can determine the cosmological constant Λˉ but also it can determine the cosmological constant Λ a…
We discuss a class of (local and non-local) theories of gravity that share same properties: i) they admit the Einstein spacetime with arbitrary cosmological constant as a solution; ii) the on-shell action of such a theory vanishes and iii) any (cosmological or black hole) horizon in the Einstein spacetime with a positi…
Researchers prove a 30-year-old cosmological conjecture about spacetime.
problem The rigidity of the cosmological Hawking--Penrose singularity theorem.
method Combining global viscosity solutions and elliptic approaches.
result A timelike geodesically complete spacetime splits isometrically as a Lorentzian product.
Researchers generalize cosmological models using Finsler geometry.
problem Cosmological models that closely approximate pseudo-Riemannian geometry.
method Identifying Lie Algebra of symmetry generators for spatially homogeneous and isotropic Finsler geometries.
result Found the most general spatially homogeneous and isotropic Berwald spacetimes.
Scattering representations simplify SBI for images without extra compression.
problem Efficiently performing simulation-based inference on images with limited data.
method Use scattering representations for compression and learning, combined with spatial averaging and expressive density estimators.
result Scattering representations provide more information than traditional methods, without requiring additional simulations.
Classifies cosmological Finsler spacetimes, finding viable non-stationary models.
problem Locating viable non-stationary Finsler spacetimes in cosmology.
method Locally classified all possible cosmological homogeneous and isotropic Landsberg-type Finsler structures in 4-dimensions.
result Identified unique Finsler, non-Berwaldian Landsberg generalization of Friedmann-Lemaitre-Robertson-Walker geometry.
The paper examines isotropic cosmological space-times with changing sectional curvature.
problem Cosmological space-times with changing sectional curvature.
method Analysis of a family of geometrically well-behaved cosmological space-times foliated by isotropic hypersurfaces.
result Only space-time isometries ensure the rigidity properties of isotropic cosmological space-times.
Simplified proof of cosmic singularity theorem using new mathematical techniques.
problem Proving cosmic singularity in expanding spacetimes with positive cosmological constant.
method Unified approach using the positive resolution of the virtual positive first Betti number conjecture.
result The theorem holds without the need for a spherical Cauchy surface.
The paper introduces a new method to characterize cosmological models using observer-based invariants.
problem Equivalence problem for cosmological models in four-dimensional gravity theories.
method Modified Cartan-Karlhede algorithm adapted to fundamental observers, including derivatives of the time-like vector field.
result A list of invariants that completely characterize cosmological models, independent of coordinates.
In this paper we propose and discuss a notion of mass for compact static metrics with positive cosmological constant. As a consequence, we characterise the de Sitter solution as the only static vacuum metric with zero mass. Finally, we show how to adapt our analysis to the case of negative cosmological constant, leadin…
New vacuum spacetimes without CMC Cauchy surfaces found.
problem Finding vacuum cosmological spacetimes without CMC Cauchy surfaces.
method Extended construction of [6] using spatial topologies M#M. result Obtained a large class of vacuum cosmological spacetimes.
New mass-type invariants for cosmological space-times.
problem Characterize de Sitter solutions in space-times with a cosmological constant.
method Introduce new mass-type invariants and prove positive mass theorems.
result 1-harmonic Mass provides new characterizations and inequalities.
The study examines conditions that prevent null geodesic lines in spacetimes, impacting cosmological geometry.
problem Preventing the existence of null geodesic lines in spacetimes.
method Identifying geometric conditions on foliations of spacetimes that prevent null geodesic lines, especially for spacetimes with compact Cauchy hypersurfaces.
result Conditions on foliations can prevent null geodesic lines, leading to restrictions on cosmological spacetime geometry.
This is the second of two works, in which we discuss the definition of an appropriate notion of mass for static metrics, in the case where the cosmological constant is positive and the model solutions are compact. In the first part, we have established a positive mass statement, characterising the de Sitter solution as…
SU(2) flat connection on 2D Riemann surface is shown to relate to the generalized twisted geometry in 3D space with cosmological constant. Various flat connection quantities on Riemann surface are mapped to the geometrical quantities in discrete 3D space. We propose that the moduli space of SU(2) flat connections on Ri…