BibTeX

@article{2212.04521v2,
Author        = {Kiyam Lin and Maximilian von Wietersheim-Kramsta and Benjamin Joachimi and Stephen Feeney},
Title         = {A simulation-based inference pipeline for cosmic shear with the
  Kilo-Degree Survey},
Eprint        = {2212.04521v2},
DOI           = {10.1093/mnras/stad2262},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {The standard approach to inference from cosmic large-scale structure data
employs summary statistics that are compared to analytic models in a Gaussian
likelihood with pre-computed covariance. To overcome the idealising assumptions
about the form of the likelihood and the complexity of the data inherent to the
standard approach, we investigate simulation-based inference (SBI), which
learns the likelihood as a probability density parameterised by a neural
network. We construct suites of simulated, exactly Gaussian-distributed data
vectors for the most recent Kilo-Degree Survey (KiDS) weak gravitational
lensing analysis and demonstrate that SBI recovers the full 12-dimensional KiDS
posterior distribution with just under $10^4$ simulations. We optimise the
simulation strategy by initially covering the parameter space by a hypercube,
followed by batches of actively learnt additional points. The data compression
in our SBI implementation is robust to suboptimal choices of fiducial parameter
values and of data covariance. Together with a fast simulator, SBI is therefore
a competitive and more versatile alternative to standard inference.},
Year          = {2022},
Month         = {Dec},
Note          = {MNRAS 524 (2023) 6167-6180},
Url           = {http://arxiv.org/abs/2212.04521v2},
File          = {2212.04521v2.pdf}
}