The application of ridgelines in extended radio source cross-identification
The application of ridgelines in extended radio source cross-identification
复制标题
山脊线在扩展射电源交叉识别中的应用
DOI：
10.1093/mnras/stab2952
复制
发表时间：
2022
影响因子：
4.8
通讯作者：
Barkus B
中科院分区：
文献类型：
--
作者：
Barkus B
扩展射电源是现代深部射电调查中的重要少数群体，因为它们能够详细调查活动星系及其环境等射电发射区的物理情况。由于其形态的复杂性和多个潜在的对应物，对这一不断扩大的群体来说，射电源与光学宿主星系的交叉识别是具有挑战性的。在低频阵列两米天文测量的第一次数据发布中，紧凑源的自动似然比得到了扩展源的公民科学视觉识别过程的补充。在本文中，我们提出了一种新的方法，通过使用脊线来自动识别扩展源的主机，该脊线跟踪假定的流经最高通量密度点的流体流动方向。将一个新的程序RL-XID应用于LoTSS DR1，我们证明了脊线是通用的；通过提供关于空间结构和亮度分布的信息，它们可以用于光学主机识别和射电测量中的形态研究。RL-XID为85%的亮度大于10微焦耳和大于15微弧秒的源绘制脊线，其子集&>30微焦耳和&>60微弧秒的性能提高了96%。使用来自LoTSS DR1的具有已知宿主的源的样本，我们证明了RL-XID成功地识别了98%的具有成功绘制的脊线的源的宿主，并且其性能与通过公民科学进行视觉识别的水平相当。我们还证明了脊线亮度轮廓为形态分类提供了一种很有前途的自动化技术。
Extended radio sources are an important minority population in modern deep radio surveys, because they enable detailed investigation of the physics governing radio-emitting regions such as active galaxies and their environments. Cross-identification of radio sources with optical host galaxies is challenging for this extended population, due to their morphological complexity and multiple potential counterparts. In the first data release of the Low-Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS DR1), the automated likelihood ratio for compact sources was supplemented by a citizen science visual identification process for extended sources. In this paper, we present a novel method for automating the host identification of extended sources by using ridgelines, which trace the assumed direction of fluid flow through the points of highest flux density. Applying a new code,RL-Xid, to LoTSS DR1, we demonstrate that ridgelines are versatile; by providing information about spatial structure and brightness distributions, they can be used both for optical host identification and morphological studies in radio surveys.RL-Xiddraws ridgelines for 85 per cent of sources brighter than 10 mJy and larger than 15 arcsec, with an improved performance of 96 per cent for the subset >30 mJy and >60 arcsec. Using a sample of sources with known hosts from LoTSS DR1, we demonstrate thatRL-Xidsuccessfully identifies the host for 98 per cent of the sources with successfully drawn ridgelines, and performs at a comparable level to visual identification via citizen science. We also demonstrate that ridgeline brightness profiles provide a promising automated technique for morphological classification.