Bayesian Simulation-based Inference for Cosmological Initial Conditions

TL;DR

This paper introduces a Bayesian simulation-based inference method with autoregressive modeling for reconstructing cosmological initial conditions from late-time density fields, capable of handling complex, non-differentiable simulators.

Contribution

It presents a novel, versatile Bayesian reconstruction algorithm that works with generic simulators and enables posterior sampling for cosmological fields.

Findings

Successful initial application to cosmological initial condition recovery

Handles non-differentiable forward simulators effectively

Provides a new tool for astrophysical field reconstruction

Abstract

Reconstructing astrophysical and cosmological fields from observations is challenging. It requires accounting for non-linear transformations, mixing of spatial structure, and noise. In contrast, forward simulators that map fields to observations are readily available for many applications. We present a versatile Bayesian field reconstruction algorithm rooted in simulation-based inference and enhanced by autoregressive modeling. The proposed technique is applicable to generic (non-differentiable) forward simulators and allows sampling from the posterior for the underlying field. We show first promising results on a proof-of-concept application: the recovery of cosmological initial conditions from late-time density fields.