Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

CosmoDS: A Python toolkit for constraining cosmological models via dynamical systems analysis with Cobaya

Nandan Roy, Prasanta Sahoo

Abstract

We present a toolkit, CosmoDS, designed to study cosmological models at the background level using dynamical system analysis within the Cobaya framework. Dynamical system analysis is a powerful mathematical approach for studying nonlinear systems and is widely used in cosmology to investigate the stability and evolution of different cosmological models, particularly those involving dark energy. In this code, we provide a framework for constraining cosmological models using a dynamical system formulation. Most importantly, the toolkit is directly integrated with the Cobaya interface, allowing users to take advantage of the sophisticated statistical and inference tools already implemented in Cobaya for cosmological parameter estimation and model analysis.

CosmoDS: A Python toolkit for constraining cosmological models via dynamical systems analysis with Cobaya

Abstract

We present a toolkit, CosmoDS, designed to study cosmological models at the background level using dynamical system analysis within the Cobaya framework. Dynamical system analysis is a powerful mathematical approach for studying nonlinear systems and is widely used in cosmology to investigate the stability and evolution of different cosmological models, particularly those involving dark energy. In this code, we provide a framework for constraining cosmological models using a dynamical system formulation. Most importantly, the toolkit is directly integrated with the Cobaya interface, allowing users to take advantage of the sophisticated statistical and inference tools already implemented in Cobaya for cosmological parameter estimation and model analysis.
Paper Structure (9 sections, 10 equations, 1 figure)

This paper contains 9 sections, 10 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Example Model Implemented
  3. Numerical Implementation
  4. Interface with Cobaya
  5. Parameter Estimation Framework
  6. Example Likelihood Analysis
  7. Code Availability
  8. Conclusion
  9. Acknowledgment

Figures (1)

  • Figure 1: 1D and 2D posterior distributions of the example model with $m=2$ obtained using CosmoDS with the combination of DESI-DR2 and DES Y5 datasets.