Hamiltonian Monte Carlo reconstruction from peculiar velocities

Abstract

The problem of the reconstruction of the large scale density and velocity fields from peculiar velocities surveys is addressed here within a Bayesian framework by means of Hamiltonian Monte Carlo (HMC) sampling. The HAmiltonian Monte carlo reconstruction of the Local EnvironmenT (Hamlet) algorithm is designed to reconstruct the linear large scale density and velocity fields in conjunction with the undoing of lognormal bias in the derived distances and velocities of peculiar velocities surveys such as the Cosmicflows data. The Hamlet code has been tested against Cosmicflows mock catalogs consisting of up to 30 000 data points with mock errors akin to those of the Cosmicflows-3 data, within the framework of the LCDM standard model of cosmology. The Hamlet code outperforms previous applications of Gibbs sampling MCMC reconstruction from the Cosmicflows-3 data by two to four orders of magnitude in CPU time. The gain in performance is due to the inherent higher efficiency of the HMC algorithm and due to parallel computing on GPUs rather than CPUs. This gain will enable an increase in the reconstruction of the large scale structure from the upcoming Cosmicfows-4 data and the setting of constrained initial conditions for cosmological high resolution simulations.

arXiv comment14 pages, 10 figures

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• Reconstructing dark matter distribution with peculiar velocities: Bayesian forward modelling with corrections for inhomogeneous Malmquist bias
• Field-Based Physical Inference From Peculiar Velocity Tracers
• Forecast for growth-rate measurement using peculiar velocities from LSST supernovae
• Testing Bayesian reconstruction methods from peculiar velocities
• Modeling the Cosmic Dispersion Measure in the D < 120 Mpc Local Universe
• Generalized framework for likelihood-based field-level inference of growth rate from velocity and density fields
• Evaluating the variance of individual halo properties in constrained cosmological simulations
• Hawai'i Supernova Flows: A Peculiar Velocity Survey Using Over a Thousand Supernovae in the Near-Infrared
• Statistically bias-minimized peculiar velocity catalogs from Gibbs point processes and Bayesian inference
• An effective $\mathbf{\Lambda }$-Szekeres modelling of the local Universe with Cosmicflows-4