In:
Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP)
Kurzfassung:
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 Occupation Distribution (HOD) models which are used to connect galaxies with their dark matter halos. Using a combination of continuous relaxation and gradient re-parameterisation techniques, we can obtain well-defined gradients with respect to HOD parameters through discrete galaxy catalogs realisations. Having access to these gradients allows us to leverage efficient sampling schemes, such as Hamiltonian Monte-Carlo, and greatly speed up parameter inference. We demonstrate our technique on a mock galaxy catalog generated from the Bolshoi simulation using a standard HOD model and find near identical posteriors as standard Markov Chain Monte Carlo techniques with an increase of ∼8x in convergence efficiency. Our differentiable HOD model also has broad applications in full forward model approaches to cosmic structure and cosmological analysis.
Materialart:
Online-Ressource
ISSN:
0035-8711
,
1365-2966
DOI:
10.1093/mnras/stae350
Sprache:
Englisch
Verlag:
Oxford University Press (OUP)
Publikationsdatum:
2024
ZDB Id:
2016084-7
SSG:
16,12
