Bayesian 3d velocity field reconstruction with VIRBIuS

TL;DR

This paper introduces VIRBIuS, a Bayesian algorithm for reconstructing the 3D velocity field of galaxies from observational data, accounting for various observational effects and enabling analysis of large galaxy surveys.

Contribution

The paper presents a novel Bayesian algorithm and software for robust 3D velocity field reconstruction from galaxy data, including handling of selection effects and non-linearities.

Findings

Robust velocity measurements for 3,000 galaxy mock catalogues

Algorithm scalable to tens of thousands of galaxies

Improves velocity field inference for future large surveys

Abstract

I describe a new Bayesian based algorithm to infer the full three dimensional velocity field from observed distances and spectroscopic galaxy catalogues. In addition to the velocity field itself, the algorithm reconstructs true distances, some cosmological parameters and specific non-linearities in the velocity field. The algorithm takes care of selection effects, miscalibration issues and can be easily extended to handle direct fitting of, e.g., the inverse Tully-Fisher relation. I first describe the algorithm in details alongside its performances. This algorithm is implemented in the VIRBIuS (VelocIty Reconstruction using Bayesian Inference Software) software package. I then test it on different mock distance catalogues with a varying complexity of observational issues. The model proved to give robust measurement of velocities for mock catalogues of 3,000 galaxies. I expect the core of the algorithm to scale to tens of thousands galaxies. It holds the promises of giving a better handle on future large and deep distance surveys for which individual errors on distance would impede velocity field inference.