How to embed any likelihood into SBI: Application to Planck + Stage IV galaxy surveys and Dynamical Dark Energy

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 $w_0{w}_a$ equation-of-state. Assuming the $w_0{w}_a$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.

9+6 pages, 6 figures, 3 tables. Added minor corrections. v2 matches the published PRD version. Comments welcome!

References in corpus (27)

• The Pantheon+ Analysis: Cosmological Constraints
• DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations
• The Pantheon+ Analysis: The Full Dataset and Light-Curve Release
• Euclid. I. Overview of the Euclid mission
• DESI DR2 Results II: Measurements of Baryon Acoustic Oscillations and Cosmological Constraints
• A Parameterized Post-Friedmann Framework for Modified Gravity
• The Atacama Cosmology Telescope: DR6 Gravitational Lensing Map and Cosmological Parameters
• The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
• Crossing the Phantom Divide with Parameterized Post-Friedmann Dark Energy
• CMB power spectra and cosmological parameters from Planck PR4 with CamSpec
• Fast likelihood-free cosmology with neural density estimators and active learning
• Hints of Nonminimally Coupled Gravity in DESI 2024 Baryon Acoustic Oscillation Measurements
• Robustness of dark energy phenomenology across different parameterizations
• Dynamical Dark Energy Beyond Planck? Constraints from multiple CMB probes, DESI BAO and Type-Ia Supernovae
• Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation
• How much information can be extracted from galaxy clustering at the field level?
• CONNECT: A neural network based framework for emulating cosmological observables and cosmological parameter inference
• Euclid preparation. Sensitivity to neutrino parameters
• SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation
• Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation
• Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation
• KiDS-SBI: Simulation-based inference analysis of KiDS-1000 cosmic shear
• Fast likelihood-free inference in the LSS Stage IV era
• Simulation-Based Inference Benchmark for Weak Lensing Cosmology
• DESIgning concordant distances in the age of precision cosmology: the impact of density fluctuations
• CombineHarvesterFlow: Joint Probe Analysis Made Easy with Normalizing Flows
• Improving CMB constraints on early Universe physics with LSS: A multi-probe forecast including cross-covariance