AI 中文总结
针对下一代射电干涉仪数据量巨大的问题，提出用稀疏高斯过程建模可见性，存储紧凑概率模型。经模拟ALMA观测验证，该方法压缩率高，能利用相关性提信噪比，还可预测、更新及过滤异常值，助力任意视场和分辨率成像。
AI 中文摘要
下一代射电干涉仪将产生海量数据，存储原始可见性测量值并随后在uv空间频率空间中合并观测变得不切实际。相似uv位置的可见性测量值测量相同信号但噪声实现不同。原则上，可通过仅存储推断的平均可见性及其不确定性来压缩这些测量值。我们提出用稀疏高斯过程（GP）对可见性进行建模并存储所得的紧凑概率模型而非原始可见性。通过模拟阿塔卡马大型毫米波/亚毫米波阵列（ALMA）观测，我们证明稀疏GP足够灵活以表示可见性并高保真恢复图像。对于8小时平方公里阵列（SKA）-Mid观测，我们估计压缩因子为10^3 - 10^5，通过扩展GP输入空间以包括光谱轴有望进一步提高压缩率。除数据压缩外，该模型利用uv空间中的相关性，与独立网格平均相比提高了信噪比。一旦训练，该模型可在任何所需uv坐标处预测可见性及其不确定性，允许以任意视场和图像分辨率进行成像。该模型还可在基于预测过滤异常值的同时用新观测进行增量更新。
英文摘要
Next-generation radio interferometers will produce massive data volumes, making it impractical to store original visibility measurements and later combine observations in $uv$ spatial frequency space. Visibility measurements at similar $uv$ locations measure the same signal but different noise realizations. In principle, these measurements can therefore be compressed by storing only the inferred mean visibility and its uncertainty. We propose modeling the visibility with a sparse Gaussian process (GP) and storing the resulting compact probabilistic model rather than raw visibilities. Using simulated Atacama Large Millimeter/submillimeter Array (ALMA) observations, we demonstrate that the sparse GP is flexible enough to represent the visibilities and recover images with high fidelity. We estimate compression factors of $10^3-10^5$ for an 8-hour Square Kilometre Array (SKA)-Mid observation, with further gains expected by extending the GP input space to include the spectral axis. Beyond data compression, the model exploits correlations in $uv$ space, boosting the signal-to-noise ratio compared with independent grid averaging. Once trained, the model can predict visibility and its uncertainty at any desired $uv$ coordinates, allowing imaging with arbitrary fields of view and image resolutions. The model may also be incrementally updated with new observations while filtering outliers based on the prediction.
Comments9 pages, 4 figures, SPIE Astronomical Telescopes + Instrumentation 2026 Paper No. 14153-10
