Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation

Abstract

Sampling-based inference techniques are central to modern cosmological data analysis; these methods, however, scale poorly with dimensionality and typically require approximate or intractable likelihoods. In this paper we describe how Truncated Marginal Neural Ratio Estimation (tmnre) (a new approach in so-called simulation-based inference) naturally evades these issues, improving the (i) efficiency, (ii) scalability, and (iii) trustworthiness of the inference. Using measurements of the Cosmic Microwave Background (CMB), we show that tmnre can achieve converged posteriors using orders of magnitude fewer simulator calls than conventional Markov Chain Monte Carlo (mcmc) methods. Remarkably, the required number of samples is effectively independent of the number of nuisance parameters. In addition, a property called local amortization allows the performance of rigorous statistical consistency checks that are not accessible to sampling-based methods. tmnre promises to become a powerful tool for cosmological data analysis, particularly in the context of extended cosmologies, where the timescale required for conventional sampling-based inference methods to converge can greatly exceed that of simple cosmological models such as ΛCDM. To perform these computations, we use an implementation of tmnre via the open-source code swyft. ‡ *

Primordial non-Gaussianity and non-Gaussian Covariance

“…Applying reconstruction methods [52] or using simulations (e.g. through simulation based inference [53]) [54][55][56][57][58], both active fields of investigation, will certainly help to establish to what degree we have to modify our analysis tools in search for signs of primordial non-Gaussianity.…”

Fisher Forecasts for Primordial non-Gaussianity from Persistent Homology

“…The tradeoff is that the likelihood for our persistence diagrams is only implicitly defined. Parameter estimation in the context of implicit likelihoods is precisely within the purview of the rapidly advancing field of simulation-based inference ( [80][81][82][83], see [84] for a recent review and [85][86][87][88][89] for cosmological applications). Within simulation-based inference it has been advocated (see e.g.…”

swyft: Truncated Marginal Neural Ratio Estimation in Python

“…The software package has enabled inference on dark matter substructure in strongly lensed galaxies (Coogan et al, 2020), estimated cosmological parameters from cosmic microwave background simulation data (Cole et al, 2021), and was cited in a white paper laying out a vision for astropartical physics research during the next decade (Batista et al, 2021). Ongoing work with swyft aims to reduce the response time to gravitational wave triggers from LIGO-Virgo by estimating the marginal posterior with unprecedented speed.…”