标题： 深空3 GHz观测使用甚大天线阵列的北洛克曼洞-1。 源提取和不确定性分析 显示英文标题

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

摘要： 这是描述使用卡尔·G·扬斯基甚大阵列对北洛克曼洞进行深度3千兆赫观测的两篇论文中的第一篇。 本文的目的是通过使用模拟图像来研究源查找程序的不确定性和准确性，以及量化由于分辨率导致的系统性效应，如源混淆和源大小。 虽然这些效应并不新鲜，但这项工作旨在作为一个特定的案例研究，可以扩展和应用于其他调查。 我们使用模拟来推导拟合参数的不确定性，并为实际目录（在第二篇论文中提出）提供偏差校正。 我们比较了两种不同的源查找程序，OBIT和AEGEAN，以及两种不同的有效分辨率，8角秒和2.75角秒。 我们发现这两种程序表现相当好，OBIT在去混源方面略胜一筹，但在拟合解析源方面稍差。 我们表明，一旦源大小超过波束，30%到70%的源会被遗漏或不准确地拟合，这可能解释了高分辨率调查中的源计数误差。 我们还研究了混合的影响，发现任何分离小于波束大小的源都会被拟合为单一源。 我们表明，使用机器学习技术可以以90%的准确率正确识别混合源，而基于先验的拟合可以将去混源的数量提高70%。 显示英文摘要

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.