Kosmulator: A Python framework for cosmological inference with MCMC
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866908822457548800 |
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| author | Hough, Renier T. Rugg, Robert Sahlu, Shambel Abebe, Amare |
| author_facet | Hough, Renier T. Rugg, Robert Sahlu, Shambel Abebe, Amare |
| contents | 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 $Λ$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 $\leq0.3σ$ statistical agreement on $H_{0}$ and $Ω_{m}$ and $<0.6\%$ precision on $χ^{2}$, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08424 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Kosmulator: A Python framework for cosmological inference with MCMC Hough, Renier T. Rugg, Robert Sahlu, Shambel Abebe, Amare Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology 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 $Λ$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 $\leq0.3σ$ statistical agreement on $H_{0}$ and $Ω_{m}$ and $<0.6\%$ precision on $χ^{2}$, 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. |
| title | Kosmulator: A Python framework for cosmological inference with MCMC |
| topic | Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2602.08424 |