Papers

Simulation-Based Inference Benchmark for LSST Weak Lensing Cosmology

J Zeghal, D Lanzieri, F Lanusse, A Boucaud… - arXiv preprint arXiv …, 2024 - arxiv.org
Astrophysics paper astro-ph.CO Suggest

… analysis techniques to match the power of upcoming telescopes, recent years have seen a paradigm shift from analytical likelihoodbased to simulation-based inference. …

Cited by Link to paper

BibTeX

@article{2409.17975v2,
Author        = {Justine Zeghal and Denise Lanzieri and François Lanusse and Alexandre Boucaud and Gilles Louppe and Eric Aubourg and Adrian E. Bayer and The LSST Dark Energy Science Collaboration},
Title         = {Simulation-Based Inference Benchmark for Weak Lensing Cosmology},
Eprint        = {2409.17975v2},
DOI           = {10.1051/0004-6361/202452410},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {Standard cosmological analysis, which relies on two-point statistics, fails
to extract the full information of the data. This limits our ability to
constrain with precision cosmological parameters. Thus, 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.
We focus on full-field inference, considered the optimal form of inference. Our
objective is to benchmark several ways of conducting full-field inference to
gain insight into the number of simulations required for each method. We make a
distinction between explicit and implicit full-field inference. Moreover, as it
is crucial for explicit full-field inference to use a differentiable forward
model, we aim to discuss the advantages of having this property for the
implicit approach. We use the sbi_lens package which provides a fast and
differentiable log-normal forward model. This 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
sampler. The latter starts by compressing the data into sufficient statistics
and uses the Neural Likelihood Estimation algorithm and the one augmented with
gradient. We perform a full-field analysis on LSST Y10 like weak lensing
simulated mass maps. We show that explicit and implicit full-field inference
yield consistent constraints. Explicit inference requires 630 000 simulations
with our particular sampler corresponding to 400 independent samples. Implicit
inference requires a maximum of 101 000 simulations split into 100 000
simulations to build sufficient statistics (this number is not fine tuned) and
1 000 simulations to perform inference. Additionally, we show that our way of
exploiting the gradients does not significantly help implicit inference.},
Year          = {2024},
Month         = {Sep},
Note          = {A&A 699, A327 (2025)},
Url           = {http://arxiv.org/abs/2409.17975v2},
File          = {2409.17975v2.pdf}
}

Share