HIDM: Emulating Large Scale HI Maps using Score-based Diffusion Models
Nov 1, 2023Citations per year
Abstract: (arXiv)
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.Note:
- Accepted to Machine Learning and the Physical Sciences Workshop, NeurIPS 2023
References(115)
Figures(4)
- [2017]