Merlin: Fast and flexible 3x2pt cosmology with simulation-based inference
Abstract
We present Merlin, a simulation-based inference (SBI) pipeline to perform cosmological analyses of 3x2pt summary statistics: cosmic shear, galaxy clustering and the cross-correlation power spectra. Our approach combines Marginal Neural Ratio Estimation (MNRE) with 3x2pt angular power spectra predictions from the cloelib library, although the pipeline is readily extensible to other cosmology libraries. We demonstrate this pipeline on a realistic setting representative of a Stage-IV photometric survey, with a 50-dimensional parameter space describing cosmology and a wide range of systematic effects. We find posteriors that are in excellent agreement with the nested sampler Nautilus, while reducing the number of required CPU-hours by two orders of magnitude. Because the generation of training data and the training of inference networks are decoupled processes, a single simulation bank can be reused to perform inference under different analysis choices, further improving the simulator-efficiency. We illustrate this flexibility by applying scale cuts, varying the survey area, and removing cosmic shear from the data vector (2x2pt), all at zero extra model evaluations, whereas sampling-based methods need costly re-runs for each case. The Merlin code is publicly available on GitHub.
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BibTeX
@misc{wernersson2026merlin,
title = {{Merlin: Fast and flexible 3x2pt cosmology with simulation-based inference}},
author = {Alexandra Wernersson and Guillermo Franco-Abellán and Guadalupe Cañas-Herrera},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.24946},
eprint = {2609.24946},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.24946},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Merlin: Fast and flexible 3x2pt cosmology with simulation-based inference
AU - Alexandra Wernersson
AU - Guillermo Franco-Abellán
AU - Guadalupe Cañas-Herrera
PY - 2026
DA - 2026/09/21
PB - arXiv
DO - 10.48550/arxiv.2609.24946
UR - https://arxiv.org/abs/2609.24946
AB - We present Merlin, a simulation-based inference (SBI) pipeline to perform cosmological analyses of 3x2pt summary statistics: cosmic shear, galaxy clustering and the cross-correlation power spectra. Our approach combines Marginal Neural Ratio Estimation (MNRE) with 3x2pt angular power spectra predictions from the cloelib library, although the pipeline is readily extensible to other cosmology libraries. We demonstrate this pipeline on a realistic setting representative of a Stage-IV photometric survey, with a 50-dimensional parameter space describing cosmology and a wide range of systematic effects. We find posteriors that are in excellent agreement with the nested sampler Nautilus, while reducing the number of required CPU-hours by two orders of magnitude. Because the generation of training data and the training of inference networks are decoupled processes, a single simulation bank can be reused to perform inference under different analysis choices, further improving the simulator-efficiency. We illustrate this flexibility by applying scale cuts, varying the survey area, and removing cosmic shear from the data vector (2x2pt), all at zero extra model evaluations, whereas sampling-based methods need costly re-runs for each case. The Merlin code is publicly available on GitHub.
ER -