HIDM: Emulating Large Scale HI Maps using Score-based Diffusion Models
Cosmology and Nongalactic Astrophysics
2023-11-03 v1 Instrumentation and Methods for Astrophysics
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.
Cite
@article{arxiv.2311.00833,
title = {HIDM: Emulating Large Scale HI Maps using Score-based Diffusion Models},
author = {Sultan Hassan and Sambatra Andrianomena},
journal= {arXiv preprint arXiv:2311.00833},
year = {2023}
}
Comments
Accepted to Machine Learning and the Physical Sciences Workshop, NeurIPS 2023