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Compressive sensing based multi-frequency synthesis

机译:基于压缩感测的多频合成

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Most modern radio telescope arrays observe with wideband receivers to optimise signal-to-noise. However for such wideband visibility data, the changing shape of the source with frequency may limit the performance of existing deconvolution methods. In such a case, it is necessary to estimate explicitly the change in brightness with frequency. This is called multifrequency synthesis (MFS). The current MFS methods either work only for the linear spectral model or take a long time to converge. We propose a new method, MFS-CS, to solve the MFS problem based on the theory of compressive sensing (CS). Experimental results show that it provides superior reconstructions compared to a normal deconvolution method (MSCLEAN) and an MFS-based extension (MFS-MSCLEAN). The main advantages of our method are improved efficiency, compatibility to any spectral model and simplicity implementation. MFS-CS is a potential candidate solution for the next generation telescope.
机译:大多数现代射电望远镜阵列都使用宽带接收器进行观测,以优化信噪比。但是,对于这种宽带可见性数据,源的形状随频率的变化可能会限制现有反卷积方法的性能。在这种情况下,必须明确估计亮度随频率的变化。这称为多频合成(MFS)。当前的MFS方法仅适用于线性光谱模型,或者需要很长时间才能收敛。我们提出了一种新的方法,MFS-CS,基于压缩感测(CS)理论来解决MFS问题。实验结果表明,与常规反卷积方法(MSCLEAN)和基于MFS的扩展(MFS-MSCLEAN)相比,它提供了卓越的重建。我们方法的主要优点是提高了效率,与任何光谱模型的兼容性以及简化的实现。 MFS-CS是下一代望远镜的潜在候选解决方案。

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