发表机构
Rice University; Rice Space Institute; National Radio Astronomy Observatory; NSF-Simons AI Institute for Cosmic Origins; University of Pennsylvania; Los Alamos National Laboratory（莱斯大学; 莱斯空间研究所; 美国国家射电天文台; NSF-西蒙斯宇宙起源人工智能研究所; 宾夕法尼亚大学; 洛斯阿拉莫斯国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SIFARI是一种自监督神经网络方法，直接拟合可见度函数以重建射电干涉图像，无需外部训练集，在合成ALMA测试和PDS 70观测中均优于CLEAN，并支持自校准。
AI 中文摘要
射电干涉图像是从稀疏采样的可见度函数中重建的，基于CLEAN的成像方法在处理空间滤波、复杂形态和不确定性量化方面可能存在问题。直接拟合可见度函数的替代方法可以解决其中一些局限性，但通常需要手动选择图像先验和模型超参数。我们提出了SIFARI（自监督干涉拟合用于天文射电成像），这是一种自监督神经网络工作流程，将天空亮度表示为位置的连续函数，并在没有外部图像训练集或显式空间正则化器的情况下拟合测量的可见度函数。经验规则在训练前根据可见度函数设置傅里叶特征尺度，控制网络拟合精细结构的容易程度。使用随机权重平均-高斯（SWAG）对网络权重进行采样，可得到近似的亮度不确定性估计，我们将其与热噪声基底相结合，构建空间分辨的信噪比图。在合成ALMA测试中，SIFARI产生的有效点源响应比自然加权CLEAN恢复波束窄约八倍，并且在缺少短基线时恢复的扩展通量比CLEAN更多。在所有三个形态基准测试中，它也比恢复的CLEAN图像实现了更高的图像保真度。应用于PDS 70的ALMA观测，SIFARI恢复了明亮的外环以及中心空腔中微弱的致密发射。对于仅长基线的WISPIT 2数据，SIFARI提供了相位自校准所需的天空模型，而CLEAN模型在此情况下不适用。恢复的、自校准的SIFARI图像的RMS噪声比未经自校准的原始可见度函数制作的CLEAN图像低约30%。
英文摘要
Radio-interferometric images are reconstructed from sparsely sampled visibilities, and CLEAN-based imaging can struggle with spatial filtering, complex morphologies, and uncertainty quantification. Alternative methods that fit visibilities directly can address some of these limitations but often require manual choices of image priors and model hyperparameters. We present SIFARI (Self-Supervised Interferometric Fitting for Astronomical Radio Imaging), a self-supervised neural network workflow that represents sky brightness as a continuous function of position and fits measured visibilities without an external image training set or explicit spatial regularizer. An empirical rule sets the Fourier feature scale from the visibilities before training, controlling how readily the network fits fine structure. Sampling network weights with Stochastic Weight Averaging-Gaussian (SWAG) gives approximate brightness uncertainty estimates, which we combine with a thermal-noise floor to construct spatially resolved signal-to-noise maps. In synthetic ALMA tests, SIFARI yields an effective point-source response about eight times narrower than the natural-weighting CLEAN restoring beam and recovers more extended flux than CLEAN when short baselines are missing. It also achieves higher image fidelity than the restored CLEAN images in all three morphology benchmarks. Applied to ALMA observations of PDS 70, SIFARI recovers the bright outer ring together with faint compact emission in the central cavity. For long-baseline-only WISPIT 2 data, SIFARI supplies a sky model for phase self-calibration where the CLEAN model is inadequate. The restored, self-calibrated SIFARI image has approximately 30% lower RMS noise than the CLEAN image made from the original visibilities without self-calibration.
Comments19 pages, 9 figures. Currently under review
