# Cosmo-Learn: code for learning cosmology using different methods and mock data

## TL;DR

Cosmo-Learn is an open-source Python package that simulates cosmological data and applies various statistical and machine learning methods for inference, aiding research and education in cosmology.

## Contribution

It introduces a flexible, modular framework for benchmarking and comparing different cosmological inference techniques using realistic simulated data.

## Key findings

- Demonstrates internal consistency of simulated data with input cosmology
- Supports multiple inference methods including MCMC, neural networks, and Gaussian processes
- Provides a standardized platform for method comparison and educational purposes

## Abstract

We present cosmo_learn, an open-source python-based software package designed to simulate cosmological data and perform data-driven inference using a range of modern statistical and machine learning techniques. Motivated by the growing complexity of cosmological models and the emergence of observational tensions, cosmo_learn provides a standardized and flexible framework for benchmarking cosmological inference methods. The package supports realistic noise modeling for key observables in the late Universe, including cosmic chronometers, supernovae Ia, baryon acoustic oscillations, redshift space distortions, and gravitational wave bright sirens. We demonstrate the internal consistency of the simulated data with the input cosmology via residuals and parameter recovery using a fiducial $w$CDM model. Built-in learning and inference modules include traditional Markov Chain Monte Carlo, as well as more recent approaches such as genetic algorithms, Gaussian processes, Bayesian ridge regression, and artificial neural networks. These methods are implemented in a modular and extensible architecture designed to facilitate comparisons across inference strategies in a common pipeline. By providing a flexible and transparent simulation and learning environment, cosmo_learn supports both educational and research efforts at the intersection of cosmology, statistics, and machine learning.