Differentiable stochastic halo occupation distribution

Horowitz, Benjamin; Hahn, ChangHoon; Lanusse, Francois; Modi, Chirag; Ferraro, Simone

doi:10.1093/mnras/stae350

Title: Differentiable stochastic halo occupation distribution

Journal Article · Fri Feb 02 00:00:00 UTC 2024 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stae350 · OSTI ID:2325365

Horowitz, Benjamin;

; Lanusse, Francois;

; Ferraro, Simone

ABSTRACT 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 that are used to connect galaxies with their dark matter haloes. Using a combination of continuous relaxation and gradient re-parametrization techniques, we can obtain well-defined gradients with respect to HOD parameters through discrete galaxy catalogue realizations. 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 catalogue 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 ∼8× in convergence efficiency. Our differentiable HOD model also has broad applications in full forward model approaches to cosmic structure and cosmological analysis.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), High Energy Physics (HEP)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2325365

Journal Information:: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 3 Vol. 529; ISSN 0035-8711

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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