Bayesian inference of cosmic density fields from non-linear, scale-dependent, and stochastic biased tracers

Understand

We present a Bayesian reconstruction algorithm to generate unbiased samples of the underlying dark matter field from halo catalogues.

• Our new contribution consists of implementing a non-Poisson likelihood including a deterministic non-linear and scale-dependent bias.
• In particular we present the Hamiltonian equations of motions for the negative binomial (NB) probability distribution function.
• This permits us to efficiently sample the posterior distribution function of density fields given a sample of galaxies using the Hamiltonian Monte Carlo technique implemented in the Argo code.