Reconstructing sky models from dirty radio images for accurate source
localization and flux estimation is crucial for studying galaxy evolution at
high redshift, especially in deep fields using instruments like the Atacama
Large Millimetre Array (ALMA). With new projects like the Square Kilometre
Array (SKA), there's a growing need for better source extraction methods.
Current techniques, such as CLEAN and PyBDSF, often fail to detect faint
sources, highlighting the need for more accurate methods. This study proposes
using stochastic neural networks to rebuild sky models directly from dirty
images. This method can pinpoint radio sources and measure their fluxes with
related uncertainties, marking a potential improvement in radio source
characterization. We tested this approach on 10164 images simulated with the
CASA tool simalma, based on ALMA's Cycle 5.3 antenna setup. We applied
conditional Denoising Diffusion Probabilistic Models (DDPMs) for sky models
reconstruction, then used Photutils to determine source coordinates and fluxes,
assessing the model's performance across different water vapor levels. Our
method showed excellence in source localization, achieving more than 90%
completeness at a signal-to-noise ratio (SNR) as low as 2. It also surpassed
PyBDSF in flux estimation, accurately identifying fluxes for 96% of sources in
the test set, a significant improvement over CLEAN+ PyBDSF's 57%. Conditional
DDPMs is a powerful tool for image-to-image translation, yielding accurate and
robust characterisation of radio sources, and outperforming existing
methodologies. While this study underscores its significant potential for
applications in radio astronomy, we also acknowledge certain limitations that
accompany its usage, suggesting directions for further refinement and research.

使用随机神经网络从粗糙的无线电图像中重建天空模型，实现准确的源定位和通量估计，为高红移下研究星系演化提供关键支持。