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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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171341512682 · Jun 202019922001200920172026
48 results for cosmological volume function

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.

We prove that the leaves of an inverse mean curvature flow provide a foliation of a future end of a cosmological spacetime NN under the necessary and sufficent assumptions that NN satisfies a future mean curvature barrier condition and a strong volume decay condition. Moreover, the flow parameter tt can be used to d…

2004-03-04abs ↗pdf ↗

We study, using Mean Curvature Flow methods, 2+1 dimensional cosmologies with a positive cosmological constant and matter satisfying the dominant and the strong energy conditions. If the spatial slices are compact with non-positive Euler characteristic and are initially expanding everywhere, then we prove that the spat…

2019-02-01abs ↗pdf ↗

In the first part of this paper we consider expanding vacuum cosmological spacetimes with a free TNT^N-action. Among them, we give evidence that Gowdy spacetimes have AVTD (asymptotically velocity term dominated) behavior for their initial geometry, in any dimension. We then give sufficient conditions to reach a simila…

2019-08-06abs ↗pdf ↗

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 NN-body simulations.
result BNNs yield well-calibrated uncertainty estimates and accurately predict cosmological parameters for ΩmΩ_m and σ8σ_8.

Study shows inflation in 3+1D cosmologies with bounded scalar potential and specific symmetry.

problem Understanding inflation in 3+1D cosmologies with specific constraints.
method Mean curvature flow and asymptotic analysis of metric variations, stress-energy tensor, and inflaton field dynamics.
result Inflation occurs in 3+1D cosmologies with specific constraints, demonstrating it is possible with inhomogeneous initial conditions.

The integral of the energy density function m\mathfrak m of a closed Robertson-Walker (RW) spacetime with source a perfect fluid and cosmological constant ΛΛ gives rise to an action functional on the space of scale functions of RW spacetime metrics. This paper studies closed RW spacetimes which are critical for this …

2019-04-18abs ↗pdf ↗

A theory of gravitation is proposed, modeled after the notion of a Ricci flow. In addition to the metric an independent volume enters as a fundamental geometric structure. Einstein gravity is included as a limiting case. Despite being a scalar-tensor theory the coupling to matter is different from Jordan-Brans-Dicke gr…

2006-02-14abs ↗pdf ↗

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…

2017-11-06abs ↗pdf ↗

We study Einstein's equation in (m+n)D(m+n)D and (1+n)D(1+n)D warped spaces (Mˉ,gˉ)(\bar{M},\bar{g}) and classify all such spaces satisfying Einstein equations Gˉ=Λˉgˉ\bar{G}=-\barΛ\bar{g}. We show that the warping function not only can determine the cosmological constant Λˉ\barΛ but also it can determine the cosmological constant ΛΛ a…

2016-10-14abs ↗pdf ↗

Deep learning and genetic algorithms speed up cosmological Bayesian inference.

problem Substantial computational demands in Bayesian inference for cosmological parameter estimation.
method Deep learning using feedforward neural networks to approximate likelihood functions dynamically, optimized with genetic algorithms.
result Significant speed-up in Bayesian inference process for cosmological models and datasets.

We consider spacetimes consisting of a manifold with Lorentzian metric and a weight function or scalar field. These spacetimes admit a Bakry-Émery-Ricci tensor which is a natural generalization of the Ricci tensor. We impose an energy condition on the Bakry-Émery-Ricci tensor and obtain singularity theorems of a cosmol…

2013-12-12abs ↗pdf ↗

New method calculates volume-renormalized mass from Hamiltonian perspective.

problem Calculating volume-renormalized mass for asymptotically hyperbolic manifolds.
method Using Michel's mass invariants and a reduced Hamiltonian perspective, the volume-renormalized mass is deduced.
result The reduced Hamiltonian recovers the volume-renormalized mass and its variations.

Let (M,g)(M,g) be a time oriented Lorentzian manifold and dd the Lorentzian distance on MM. The function τ(q):=supp<qd(p,q)τ(q):=\sup_{p< q} d(p,q) is the cosmological time function of MM, where as usual p<qp< q means that pp is in the causal past of qq. This function is called regular iff τ(q)<τ(q) < \infty for all qq and also $τ\to 0…

1997-09-30abs ↗pdf ↗

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.

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.

Proves limit curve theorem for incomplete metric spaces, applies to null distance in Lorentzian manifolds.

problem Control of Lorentzian lengths of limit curves in incomplete metric spaces.
method Proves limit curve theorem for incomplete metric spaces and applies to null distance.
result Strong control on Lorentzian lengths of limit curves in Sormani and Vegas' null distance.

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.

Genetic algorithms optimize neural networks for cosmological data analysis.

problem Inaccurate results from neural networks due to poor hyperparameter selection.
method Used genetic algorithms to optimize hyperparameters of neural networks.
result Genetic algorithms improve neural network performance in cosmological data analysis.

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.

This paper is motivated by the non-linear stability problem for the expanding region of Kerr de Sitter cosmologies in the context of Einstein's equations with positive cosmological constant. We show that under dynamically realistic assumptions the conformal Weyl curvature of the spacetime decays towards future null inf…

2016-10-13abs ↗pdf ↗

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.

Study on electrostatic systems with boundary, proving new geometric inequalities.

problem Electrostatic systems with boundary in higher dimensions.
method Investigation of electrostatic systems on compact manifolds with boundary, establishing new geometric properties.
result Proved sharp boundary estimates and isoperimetric-type inequalities for electrostatic manifolds.

Study compares MCMC and nested sampling for high-dimensional physics problems.

problem Efficiently sampling high-dimensional Bayesian posterior distributions in particle physics and cosmology.
method Review and comparison of MCMC and nested sampling techniques on high-dimensional test functions and real physics examples.
result Modern MCMC algorithms can outperform nested sampling in certain cases, highlighting implementation details.

This paper gives a new proof that maximal, globally hyperbolic, flat spacetimes of dimension n3n\geq 3 with compact Cauchy hypersurfaces are globally foliated by Cauchy hypersurfaces of constant mean curvature, and that such spacetimes admit a globally defined constant mean curvature time function precisely when they a…

2006-04-22abs ↗pdf ↗

For a stable marginally outer trapped surface (MOTS) in an axially symmetric spacetime with cosmological constant Λ>0Λ> 0 and with matter satisfying the dominant energy condition, we prove that the area AA and the angular momentum JJ satisfy the inequality 8πJA(1ΛA/4π)(1ΛA/12π)8π|J| \le A\sqrt{(1-ΛA/4π)(1-ΛA/12π)} which is saturated pre…

2015-01-28abs ↗pdf ↗

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.

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.

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.

The study explores Hesse manifolds and their symmetries in multifield cosmological models.

problem Understanding symmetries in multifield cosmological models.
method Analyzes Hesse functions and their properties on Riemannian manifolds.
result Complete Hesse manifolds are characterized by their index and are hyperbolic.

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.