论文标题
检测大h {\ sc i}〜光谱立方体中的星系
Detecting galaxies in a large H{\sc i}~spectral cube
论文作者
论文摘要
即将到来的平方公里阵列（SKA）预计将产生大量的数据，以进行H {\ sc i}〜科学。我们已经开发了基于MPI的{\ sc Python}管道，以使用当前的计算资源有效地处理大数据。我们的管道将如此大的h {\ sc i} 〜21 cm光谱群划分为几个小立方体，然后使用公开可用的h {\ sc i}〜源查找器{\ sc sofia- $ 2 $}并行处理它们。该管道还照顾了在群岛边界处的来源，并滤除了错误和冗余检测。通过使用真实的源目录，我们发现检测效率取决于{\ sc sofia- $ 2 $}参数，例如平滑内核大小，链接长度和阈值值。我们发现，所有通量箱的最佳内核尺寸分别在$ 3 $至$ 5 $的像素和$ 7 $至$ 15 $的像素之间，分别在空间和频率方向上。将恢复的源参数与原始值进行比较，我们发现{\ sc sofia- $ 2 $}的输出高度取决于内核大小，并且单个内核的选择不足以满足所有类型的H {\ sc i} 〜Galaxies。我们还建议使用替代方法来{\ sc sofia- $ 2 $}，可以在我们的管道中使用，以更稳定地找到来源。
The upcoming Square Kilometer Array (SKA) is expected to produce humongous amount of data for undertaking H{\sc i}~science. We have developed an MPI-based {\sc Python} pipeline to deal with the large data efficiently with the present computational resources. Our pipeline divides such large H{\sc i}~21-cm spectral cubes into several small cubelets, and then processes them in parallel using publicly available H{\sc i}~source finder {\sc SoFiA-$2$}. The pipeline also takes care of sources at the boundaries of the cubelets and also filters out false and redundant detections. By comapring with the true source catalog, we find that the detection efficiency depends on the {\sc SoFiA-$2$} parameters such as the smoothing kernel size, linking length and threshold values. We find the optimal kernel size for all flux bins to be between $3$ to $5$ pixels and $7$ to $15$ pixels, respectively in the spatial and frequency directions. Comparing the recovered source parameters with the original values, we find that the output of {\sc SoFiA-$2$} is highly dependent on kernel sizes and a single choice of kernel is not sufficient for all types of H{\sc i}~galaxies. We also propose use of alternative methods to {\sc SoFiA-$2$} which can be used in our pipeline to find sources more robustly.
