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.

翻译：从含噪射电图像中重建天空模型以实现精确的源定位和流量估计，对研究高红移星系演化至关重要，尤其是在使用阿塔卡马大型毫米/亚毫米阵列（ALMA）等设备的深场观测中。随着平方公里阵列（SKA）等新项目的推进，对更优的源提取方法需求日益增长。现有技术如CLEAN和PyBDSF常难以检测微弱源，凸显了开发更精确方法的必要性。本研究提出利用随机神经网络直接从含噪图像重建天空模型。该方法可定位射电源并测量其流量及相关不确定性，标志着射电源表征领域的潜在进步。我们在基于ALMA Cycle 5.3天线配置、使用CASA工具simalma模拟的10164张图像上进行了测试。采用条件去噪扩散概率模型（DDPMs）重建天空模型，随后利用Photutils确定源坐标与流量，并评估了模型在不同水汽含量下的性能。我们的方法在源定位方面表现卓越，在信噪比（SNR）低至2时仍可实现超过90%的完备性。在流量估计上，该方法亦优于PyBDSF，能准确识别测试集中96%源的流量，较CLEAN+PyBDSF的57%有显著提升。条件DDPMs是图像到图像转换的有力工具，能精准稳健地表征射电源，并超越现有方法。尽管本研究凸显了其在射电天文学中的巨大应用潜力，我们也承认其使用中的某些局限性，并提出了进一步优化与研究的方向。