BibTeX

@article{2304.05281v2,
Author        = {Bingjie Wang and Joel Leja and V. Ashley Villar and Joshua S. Speagle},
Title         = {SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for
  Astronomical Applications},
Eprint        = {2304.05281v2},
DOI           = {10.3847/2041-8213/ace361},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {Flagship near-future surveys targeting $10^8-10^9$ galaxies across cosmic
time will soon reveal the processes of galaxy assembly in unprecedented
resolution. This creates an immediate computational challenge on effective
analyses of the full data-set. With simulation-based inference (SBI), it is
possible to attain complex posterior distributions with the accuracy of
traditional methods but with a $>10^4$ increase in speed. However, it comes
with a major limitation. Standard SBI requires the simulated data to have
identical characteristics to the observed data, which is often violated in
astronomical surveys due to inhomogeneous coverage and/or fluctuating sky and
telescope conditions. In this work, we present a complete SBI-based
methodology, ``SBI$^{++}$,'' for treating out-of-distribution measurement
errors and missing data. We show that out-of-distribution errors can be
approximated by using standard SBI evaluations and that missing data can be
marginalized over using SBI evaluations over nearby data realizations in the
training set. In addition to the validation set, we apply SBI$^{++}$ to
galaxies identified in extragalactic images acquired by the James Webb Space
Telescope, and show that SBI$^{++}$ can infer photometric redshifts at least as
accurately as traditional sampling methods and crucially, better than the
original SBI algorithm using training data with a wide range of observational
errors. SBI$^{++}$ retains the fast inference speed of $\sim$1 sec for objects
in the observational training set distribution, and additionally permits
parameter inference outside of the trained noise and data at $\sim$1 min per
object. This expanded regime has broad implications for future applications to
astronomical surveys.},
Year          = {2023},
Month         = {Apr},
Note          = {The Astrophysical Journal Letters, 952, L10 (2023)},
Url           = {http://arxiv.org/abs/2304.05281v2},
File          = {2304.05281v2.pdf}
}