Kosmulator: A Python framework for cosmological inference with MCMC

Abstract

We present Kosmulator, a modular and vectorised Python framework designed to accelerate the statistical testing of cosmological models. As the theoretical landscape expands beyond standard $\hat{I}›$CDM, implementing new expansion histories into traditional Einstein--Boltzmann solvers becomes a significant computational bottleneck. Kosmulator addresses this by leveraging array-native execution and efficient ensemble slice sampling (via Zeus) to perform rapid Bayesian inference. We validate the framework against the industry-standard Cobaya code using a combination of Type Ia Supernovae, Cosmic Chronometers, and Baryon Acoustic Oscillation (BAO) data. Our results demonstrate that Kosmulator reproduces Cobaya's posterior constraints to within $\le 0.3\ddot{I}ƒ$ statistical agreement on $H_0$ and $\hat{I}{\text{c◯}}_m$ and $<0.6\%$ precision on $\ddot{I}{‡}^2$, while achieving a $\sim 4.5\times$ reduction in wall-clock time on a single CPU core compared to a standard MPI-parallelised baseline. Furthermore, we showcase the framework's utility by constraining the implicit power-law $f(Q)$ "$f_1$CDM" model and demonstrating its automated model selection capabilities (AIC/BIC). Kosmulator is introduced as a "scientific sieve" for rapid hypothesis testing, allowing researchers to efficiently filter theoretical candidates before deploying high-precision resources.

12 pages, 1 figure, 2 tables, 3 Python listings, Submitted for publication in the South African Gravity Society (SAGS) 2025 conference proceedings