HIDM: Emulating Large Scale HI Maps using Score-based Diffusion Models

TL;DR

HIDM utilizes score-based diffusion models to efficiently generate high-fidelity large-scale HI maps that closely match simulation data, aiding analysis of upcoming large-scale surveys.

Contribution

This work introduces HIDM, a novel application of score-based diffusion models for high-fidelity, large-scale HI map generation from simulations.

Findings

HIDM accurately reproduces the power spectrum of HI maps.

HIDM matches the probability distribution and likelihood up to second moments.

HIDM demonstrates efficiency in generating large-scale HI maps.

Abstract

Efficiently analyzing maps from upcoming large-scale surveys requires gaining direct access to a high-dimensional likelihood and generating large-scale fields with high fidelity, which both represent major challenges. Using CAMELS simulations, we employ the state-of-the-art score-based diffusion models to simultaneously achieve both tasks. We show that our model, HIDM, is able to efficiently generate high fidelity large scale HI maps that are in a good agreement with the CAMELS's power spectrum, probability distribution, and likelihood up to second moments. HIDM represents a step forward towards maximizing the scientific return of future large scale surveys.