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 $\Lambda$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 $\leq0.3\sigma$ statistical agreement on $H_{0}$ and $\Omega_{m}$ and $<0.6\%$ precision on $\chi^{2}$, 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.