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arXiv 1603.03084astro-ph.GA
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用甚大天线阵-1对洛克曼洞北的深3千兆赫观测。源提取和不确定性分析

Deep 3 GHz Observations of the Lockman Hole North with the Very Large Array - 1. Source extraction and uncertainty analysis

Tessa Vernstrom, Douglas Scott, Jasper Wall, Jim Condon, Bill Cotton, Rick Perley

摘要:

这是两篇论文中的第一篇,描述了使用卡尔G。Jansky甚大阵。本文的目的是调查,通过使用模拟图像,源查找例程的不确定性和准确性,以及量化系统的影响,由于分辨率,如源混淆和源的大小。虽然这些影响并不新鲜,但这项工作旨在作为一个特殊的案例研究,可以扩展并转化为其他调查。我们使用模拟来获得拟合参数的不确定性,以及实际目录的偏差校正(在论文2中介绍)。我们比较两种不同的源查找例程,OBIT和AEGEAN,和两种不同的有效分辨率,8和2.75弧秒。我们发现,这两个例程执行得很好,OBIT在去混合源方面稍好,但在拟合解析源方面稍差。我们发现,30%至70%的源被错过或不准确的配合,一旦源的大小变得大于光束,可能解释源计数误差在高分辨率的调查。我们还研究了混合的效果,发现任何源的分离小于光束大小适合作为单一的来源。我们表明,使用机器学习技术可以正确识别混合源高达90%的时间,和先验驱动的拟合可以导致70%的改善,在去混合源的数量。

英文摘要:

This is the first of two papers describing the observations and cataloguing of deep 3-GHz observations of the Lockman Hole North using the Karl G. Jansky Very Large Array. The aim of this paper is to investigate, through the use of simulated images, the uncertainties and accuracy of source-finding routines, as well as to quantify systematic effects due to resolution, such as source confusion and source size. While these effects are not new, this work is intended as a particular case study that can be scaled and translated to other surveys. We use the simulations to derive uncertainties in the fitted parameters, as well as bias corrections for the actual catalogue (presented in Paper 2). We compare two different source-finding routines, OBIT and AEGEAN, and two different effective resolutions, 8 and 2.75 arcsec. We find that the two routines perform comparably well, with OBIT being slightly better at de-blending sources, but slightly worse at fitting resolved sources. We show that 30 to 70 per cent of sources are missed or fit inaccurately once the source size becomes larger than the beam, possibly explaining source count errors in high-resolution surveys. We also investigate the effect of blending, finding that any sources with separations smaller than the beam size are fit as single sources. We show that the use of machine-learning techniques can correctly identify blended sources up to 90 per cent of the time, and prior-driven fitting can lead to a 70 per cent improvement in the number of de-blended sources.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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