Towards Accurate Field-Level Inference of Massive Cosmic Structures

TL;DR

This paper develops a two-step Bayesian framework to improve the accuracy of field-level inference of cosmic structures' masses, combining approximate and full N-body simulations to validate mass estimates within the local universe.

Contribution

It introduces a novel two-step inference method that leverages large-scale flow information and posterior resimulations to accurately estimate cluster and void masses.

Findings

20-step COLA integrator accurately models large-scale density fields

Posterior resimulations with N-body dynamics yield consistent mass estimates

Local mass functions align with ΛCDM predictions

Abstract

We investigate the accuracy requirements for field-level inference of cluster and void masses using data from galaxy surveys. We introduce a two-step framework that takes advantage of the fact that cluster masses are determined by flows on larger scales than the clusters themselves. First, we determine the integration accuracy required to perform field-level inference of cosmic initial conditions on these large scales, by fitting to late-time galaxy counts using the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm. A 20-step COLA integrator is able to accurately describe the density field surrounding the most massive clusters in the Local Super-Volume ($<135\,h^{-1}\,\mathrm{Mpc}$), but does not by itself lead to converged virial mass estimates. Therefore we carry out `posterior resimulations', using full $N$-body dynamics while sampling from the inferred initial conditions, and thereby obtain estimates of masses for nearby massive clusters. We show that these are in broad agreement with existing estimates, and find that mass functions in the Local Super-Volume are compatible with $\Lambda$CDM.