الأكوان الافتراضية ستروي قصة الكون بدقة أكبر سريع

الملخص الأصلي

كما كتبه المؤلفون، بالإنجليزية arXiv:2605.26210

Extracting maximum cosmological information from current and upcoming large-scale structure data requires going beyond summary statistics as currently used in likelihood-based inference. Simulation-Based Inference (SBI) promises to enable the exploitation of field-level information and the rich physics of modern hydrodynamical simulations. We develop a proof-of-concept SBI pipeline to explore its potential to constrain the cosmological parameters $$\{\Omega_{\rm m}, \sigma_8\}$$ from galaxy number counts, neutral hydrogen (HI) intensity mapping and their combination. We use neural emulators trained on full hydrodynamical simulations to generate galaxy and HI maps from fast, approximate dark matter simulations. Combined with neural posterior estimation, this enables the estimation of cosmological parameters while marginalizing over astrophysical effects. We perform inference both on the power spectrum and on representations derived from field-level 2D or 3D maps, comparing results from each probe and the combination of both tracers, and assessing the impact of data compression and multi-tracers information on cosmological constraints. Combining galaxy and HI fields improves constraints with respect to single-tracer cases by a factor 2 to 7 in terms of a Figure of Merit describing the joint precision on cosmological parameters, depending on the tracer/configuration. Moving from summary statistics to field-level inference leads to a consistent gain in constraining power of about a factor 3, with 3D maps providing the most precise and well-calibrated posteriors. This gain in precision is robust even when astrophysical parameters are marginalized over. Further developments (including realistic survey effects and improvements in emulators' faithfulness) will enable the application of this analysis pipeline to upcoming surveys.