Differentiable Stochastic Halo Occupation Distribution

Understand

In this work, we demonstrate how differentiable stochastic sampling techniques developed in the context of deep Reinforcement Learning can be used to perform efficient parameter inference over stochastic, simulation-based, forward models.

• As a particular example, we focus on the problem of estimating parameters of Halo Occupancy Distribution (HOD) models which are used to connect galaxies with their dark matter halos.
• Using a combination of continuous relaxation and gradient parameterization techniques, we can obtain well-defined gradients with respect to HOD parameters through discrete galaxy catalogs realizations.
• Having access to these gradients allows us to leverage efficient sampling schemes, such as Hamiltonian Monte-Carlo, and greatly speed up parameter inference.