Constrained cosmological simulations of the Local Group using Bayesian hierarchical field-level inference
Ewoud Wempe, Guilhem Lavaux, Simon D.M. White, Amina Helmi, Jens, Jasche, Stephen Stopyra

TL;DR
This paper introduces a Bayesian hierarchical inference method to simulate the Local Group in detail, accurately constraining galaxy masses and structures within a cosmological framework.
Contribution
It extends the BORG algorithm with multi-resolution capabilities to resolve individual galaxies and incorporate diverse observational data for detailed Local Group modeling.
Findings
Estimated MW and M31 masses with high precision.
Revealed the nearly radial orbit of the M31-MW pair.
Showed the Local Group's structure aligns with the Supergalactic Plane.
Abstract
We present a novel approach based on Bayesian field-level inference capable of resolving individual galaxies within the Local Group (LG), enabling detailed studies of its structure and formation via posterior simulations. We extend the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm with a multi-resolution approach, allowing us to reach smaller mass scales and apply observational constraints based on LG galaxies. Our updated data model simultaneously accounts for observations of mass tracers within the dark haloes of the Milky Way (MW) and M31, their observed separation and relative velocity, and the quiet surrounding Hubble flow represented through the positions and velocities of galaxies at distances from one to four Mpc. Our approach delivers representative posterior samples of CDM realisations that are statistically and simultaneously consistent with all these…
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