论文标题
radiolensfit:用于使用SKA精确星系形状测量的HPC工具
RadioLensfit: an HPC Tool for Accurate Galaxy Shape Measurement with SKA
论文作者
论文摘要
新一代射电望远镜(例如平方公里阵列(SKA))有望达到足够的灵敏度和分辨率,以提供大量已解决的微弱源,因此可以向无线电带打开弱的重力镜头观察。在本文中,我们介绍了RadiolensFit,这是一种开源工具,用于用于无线电弱透镜剪切的高效且快速的星系形状测量。在用天空模型和刻面技术隔离源可见性之后,它在傅立叶域中执行单个源模型拟合。这种方法使得实际尺寸的无线电数据集可在该域中的分析中访问,在该域中,数据尚未受到非线性成像过程引入的系统学的影响。我们详细介绍了代码的实现,并讨论了源提取算法的局限性。我们描述了代码的混合并行MPI+OpenMP,该MPI+OpenMP实施,以利用多节点HPC基础架构来加速计算并处理非常大的数据集,这些数据集可能无法完全存储在单个处理器的内存中。最后,我们在SKA-MID模拟数据集上的测量准确性和代码可伸缩性方面介绍了性能结果。特别是,我们比较了SKA 1阶段中预期源密度的1000个源的形状测量值与先前工作中从同一数据集获得的形状测量值通过原始可见性数据的联合拟合,并表明在计算时间高度降低时结果是可比的。
The new generation radio telescopes, such as the Square Kilometre Array (SKA), are expected to reach sufficient sensitivity and resolution to provide large number densities of resolved faint sources, and therefore to open weak gravitational lensing observations to the radio band. In this paper we present RadioLensfit, an open-source tool for an efficient and fast galaxy shape measurement for radio weak lensing shear. It performs a single source model fitting in the Fourier domain, after isolating the source visibilities with a sky model and a faceting technique. This approach makes real sized radio datasets accessible to an analysis in this domain, where data is not yet affected by the systematics introduced by the non-linear imaging process. We detail the implementation of the code and discuss limitations of the source extraction algorithm. We describe the hybrid parallelization MPI+OpenMP of the code, implemented to exploit multi-node HPC infrastructures for accelerating the computation and dealing with very large datasets that possibly cannot entirely be stored in the memory of a single processor. Finally, we present performance results both in terms of measurement accuracy and code scalability on SKA-MID simulated datasets. In particular, we compare shape measurements of 1000 sources at the expected source density in SKA Phase 1 with the ones obtained from the same dataset in a previous work by a joint fitting of the raw visibility data, and show that results are comparable while the computational time is highly reduced.