Field-level weak lensing cosmology with 60 simulations using multifidelity simulation-based inference

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ID: 327760
2026
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Ranked #821 of 1,339 articles by views in monthly notices of the royal astronomical society

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Abstract
Abstract We perform a realistic KiDS-Legacy mock analysis with field-level neural compression and simulation-based inference using just 60 N-body simulations. The weak lensing shear field encodes substantially more cosmological information than standard two-point summary statistics such as the power spectrum. Field-level inference can fully exploit this information, but physical realism at the field-level requires very high-fidelity simulations. This poses a major challenge for simulation-based inference (SBI): accurate empirical density modelling and deep-learning-based neural compression require tens of thousands of training samples, but achieving physical realism at the field level makes each simulation extremely costly. We demonstrate that multifidelity SBI can alleviate this tension by substantially reducing the number of high-fidelity simulations needed for accurate cosmological inference. We pre-train neural inference models on realistic KiDS-Legacy-like shear mocks using fast log-normal GLASS simulations and fine-tune them on a small set of high-fidelity N-body simulations. We show that 60 high-fidelity simulations are sufficient to obtain informative and well-calibrated cosmological posteriors, enabling at least an order-of-magnitude reduction in simulation cost for accurate field-level inference in a realistic setting.
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openalex_W7211970097 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Alex Saoulis, Kiyam Lin, N Jeffrey, Maximilian von Wietersheim-Kramsta, Davide Piras, A. Spurio Mancini, Ana M G Ferreira, Benjamin Joachimi
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Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag1702
URL
https://doi.org/10.1093/mnras/stag1702
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