BibTeX

@article{2507.22990v1,
Author        = {Guillermo Franco Abellán and Noemi Anau Montel and Oleg Savchenko and Christoph Weniger},
Title         = {How to embed any likelihood into SBI: Application to Planck + Stage IV
  galaxy surveys and Dynamical Dark Energy},
Eprint        = {2507.22990v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {Simulation-based inference (SBI) allows fast Bayesian inference for
simulators encoding implicit likelihoods. However, some explicit likelihoods
cannot be easily reformulated as simulators, hindering their integration into
combined analyses within SBI frameworks. One key example in cosmology is given
by the Planck CMB likelihoods. We present a simple method to construct an
effective simulator for any explicit likelihood using samples from a previously
converged Markov Chain Monte Carlo (MCMC) run. This effective simulator can
subsequently be combined with any forward simulator. To illustrate this method,
we combine the full Planck CMB likelihoods with a 3x2pt simulator (cosmic
shear, galaxy clustering and their cross-correlation) for a Stage IV survey
like Euclid, and test evolving dark energy parameterized by the $w_0w_a$
equation-of-state. Assuming the $w_0w_a$CDM cosmology hinted by DESI BAO DR2 +
Planck 2018 + PantheonPlus SNIa datasets, we find that future 3x2pt data alone
could detect evolving dark energy at $5\sigma$, while its combination with
current CMB, BAO and SNIa datasets could raise the detection to almost
$7\sigma$. Moreover, thanks to simulation reuse enabled by SBI, we show that
our joint analysis is in excellent agreement with MCMC while requiring zero
Boltzmann solver calls. This result opens up the possibility of performing
massive global scans combining explicit and implicit likelihoods in a highly
efficient way.},
Year          = {2025},
Month         = {Jul},
Url           = {http://arxiv.org/abs/2507.22990v1},
File          = {2507.22990v1.pdf}
}