| Title: |
Simulation-based inference benchmark for weak lensing cosmology |
| Authors: |
Zeghal, Justine; Lanzieri, Denise; Lanusse, François; Boucaud, Alexandre; Louppe, Gilles; Aubourg, Eric; Bayer, Adrian E.; LSST Dark Energy Science Collaboration |
| Source: |
Astronomy and Astrophysics, 699, A327 (2025-07) |
| Publisher Information: |
EDP Sciences |
| Publication Year: |
2025 |
| Collection: |
University of Liège: ORBi (Open Repository and Bibliography) |
| Subject Terms: |
Gravitational lensing: weak; Large-scale structure of Universe; Methods: statistical; Forward modeling; Inference methods; Large scale structure of universe; Likelihood estimation; Log-normal; Methods:statistical; Sufficient statistics; Two point statistics; Weak lensing; Astronomy and Astrophysics; Space and Planetary Science; astro-ph.CO; astro-ph.IM; Physical; chemical; mathematical & earth Sciences; Space science; astronomy & astrophysics; Physique; chimie; mathématiques & sciences de la terre; Aérospatiale; astronomie & astrophysique |
| Description: |
peer reviewed ; Context. Standard cosmological analysis, which is based on two-point statistics, fails to extract all the information embedded in the cosmological data. This limits our ability to precisely constrain cosmological parameters. Through willingness to use modern analysis techniques to match the power of upcoming telescopes, recent years have seen a paradigm shift from analytical likelihood-based to simulation-based inference. However, such methods require a large number of costly simulations. Aims. We focused on full-field inference, which is considered the optimal form of inference as it enables the recovery of cosmological constraints from simulations without any loss of cosmological information. Our objective is to review and benchmark several ways of conducting full-field inference to gain insight into the number of simulations required for each method. Specifically, we made a distinction between explicit inference methods that require an explicit form of the likelihood, such that it can be evaluated and thus sampled through sampling schemes and implicit inference methods that can be used when only an implicit version of the likelihood is available through simulations. Moreover, it is crucial for explicit full-field inference to use a differentiable forward model. Similarly, we aim to discuss the advantages of having differentiable forward models for implicit full-field inference. Methods. We used the sbi_lens package (https://github.com/DifferentiableUniverseInitiative/sbi_lens), which provides a fast and differentiable log-normal forward model to generate convergence maps mimicking a simplified version of LSST Y10 quality. While the analyses use a simplified forward model, the goal is to illustrate key methodologies and their implications. Specifically, this fast-forward model enables us to compare explicit and implicit full-field inference with and without gradient. The former is achieved by sampling the forward model through the No U-Turns (NUTS) sampler. The latter starts by compressing the ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| ISSN: |
0004-6361; 1432-0746 |
| Relation: |
https://www.aanda.org/10.1051/0004-6361/202452410/pdf; urn:issn:0004-6361; urn:issn:1432-0746; https://orbi.uliege.be/handle/2268/340499; info:hdl:2268/340499; arXiV:2409.17975v2 |
| DOI: |
10.1051/0004-6361/202452410 |
| Availability: |
https://orbi.uliege.be/handle/2268/340499; https://orbi.uliege.be/bitstream/2268/340499/1/aa52410-24.pdf; https://doi.org/10.1051/0004-6361/202452410 |
| Rights: |
open access ; http://purl.org/coar/access_right/c_abf2 ; info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.7C8EA9D8 |
| Database: |
BASE |