Speaker
Description
Simulation-based inference (SBI) allows amortized Bayesian inference for simulators with implicit likelihoods. However, some explicit likelihoods cannot easily be reformulated as simulators, hindering its integration into combined analyses within the SBI framework. One key example in cosmology is given by the Planck CMB likelihoods.
In this talk, I will present a simple method to construct an effective simulator for any explicit likelihood using posterior samples from a previously converged MCMC run. To illustrate this method, I combine the full Planck CMB likelihoods with a simulator for an Euclid-like photometric galaxy survey, and test evolving dark energy parameterized by the w0wa equation-of-state. Assuming the best-fit w0waCDM cosmology hinted by DESI BAO DR2 + Planck 2018 + PantheonPlus SNIa datasets, I show that the inclusion of future Euclid photometric data could rise the detection of evolving dark energy from 3σ to 7σ.
Moreover, I will show that the joint SBI analysis is in excellent agreement with MCMC, while requiring orders of magnitude fewer simulator calls. This result opens up the possibility of performing massive global scans combining explicit and implicit likelihoods in a hyper-efficient way.
| AI keywords | Simulation-based inference; Likelihood-based inference; AI for handling nuisance parameters |
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