Using diffusion models for cosmological parameter inference

Paper Title:

Diffusion-HMC: Parameter Inference with Diffusion Model driven Hamiltonian Monte Carlo

Published on:

8 May 2024

Primary Category:

Cosmology and Nongalactic Astrophysics

Paper Authors:

Nayantara Mudur,

Carolina Cuesta-Lazaro,

Douglas P. Finkbeiner

Bullets

Key Details

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Trains diffusion model to emulate density fields conditional on cosmology

•

Uses model likelihood for accurate cosmology parameter inference

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Demonstrates approach is more robust to noise than baseline methods

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Model captures statistics of true distribution well

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Constraints much tighter than from power spectrum alone

Explore the topics in this paper

cosmology

density fields

diffusion models

likelihood estimation

parameter inference

AI generated summary

Using diffusion models for cosmological parameter inference

This paper trains a diffusion model to generate simulations of cosmic density fields based on input cosmological parameters. It then uses the model's likelihood estimate to infer those parameters from a given density field, showing this approach yields tight constraints and is robust to noise perturbations.