论文标题

对事件范围望远镜获得的黑洞图像的离散化和过滤效果

Discretization and Filtering Effects on Black Hole Images Obtained with the Event Horizon Telescope

论文作者

Psaltis, Dimitrios, Medeiros, Lia, Lauer, Tod R., Chan, Chi-Kwan, Ozel, Feryal

论文摘要

干涉仪(例如事件范围望远镜(EHT))不会直接观察源的图像,而是以离散的空间频率测量其傅立叶组件,最大为由数组中最长基线设置的最大值。从傅立叶组件中构建图像或使用高分辨率模型进行分析的图像需要仔细的良好源结构,名义上超出了阵列分辨率。预计的主要EHT目标SGR A*和M87具有黑色孔阴影,边缘锋利,并且在周围等离子体上的丝状发射强,比尺度上的尺度要比当前最大的基线所探测的尺度要小得多。我们表明,要使不影响使用正则化最大似然方法和模型图像重建的图像,这些图像与数据直接进行了比较,则需要比阵列中最大的基线所探测的这些图像的采样(即它们的像素间距)要明显明显。使用黑洞图像的GRMHD模拟,我们估计最大允许的像素间距大约等于(1/8)gm/c^2;对于两个主要的EHT靶标,这对应于<0.5 microarcseconds的角像素大小。随后,我们主张使用二阶Butterworth滤波器,其截止比例等于最大阵列基线,作为可视化重建图像的最佳选择。与传统的高斯滤波器相反,该Butterworth滤波器保留了大部分功率在阵列探测的尺度上,同时抑制了不存在数据的精细图像详细信息。

Interferometers, such as the Event Horizon Telescope (EHT), do not directly observe the images of sources but rather measure their Fourier components at discrete spatial frequencies up to a maximum value set by the longest baseline in the array. Construction of images from the Fourier components or analysis of them with high-resolution models requires careful treatment of fine source structure nominally beyond the array resolution. The primary EHT targets, Sgr A* and M87, are expected to have black-hole shadows with sharp edges and strongly filamentary emission from the surrounding plasma on scales much smaller than those probed by the currently largest baselines. We show that for aliasing not to affect images reconstructed with regularized maximum likelihood methods and model images that are directly compared to the data, the sampling of these images (i.e., their pixel spacing) needs to be significantly finer than the scale probed by the largest baseline in the array. Using GRMHD simulations of black-hole images, we estimate the maximum allowable pixel spacing to be approximately equal to (1/8)GM/c^2; for both of the primary EHT targets, this corresponds to an angular pixel size of <0.5 microarcseconds. With aliasing under control, we then advocate use of the second-order Butterworth filter with a cut-off scale equal to the maximum array baseline as optimal for visualizing the reconstructed images. In contrast to the traditional Gaussian filters, this Butterworth filter retains most of the power at the scales probed by the array while suppressing the fine image details for which no data exist.

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