标题： ASKAP/EMU源找到数据挑战赛 显示英文标题

标题： The ASKAP/EMU Source Finding Data Challenge

摘要： 宇宙进化图（EMU）是一项提议的利用澳大利亚平方公里阵列探路者（ASKAP）对南半球至+30度银纬的射电连续谱巡天项目。EMU将采用一种已被证明是最优的自动化源识别和测量方法，以最大化最终射电源目录的可靠性、实用性和稳健性。 为了实现这一目标，进行了“数据挑战”，为国际团队提供了一个测试一系列模拟图像上不同源查找器的机会。其目的是量化现有自动化源查找和测量方法的准确性，并确定潜在的局限性。 此次挑战吸引了九个独立团队，他们测试了十一个不同的源查找工具。此外，挑战发起人还测试了当前的ASKAPsoft源查找工具，以确定它可以如何从其他工具的成功特性中受益。 本文介绍了数据挑战的结果，指出了当前一代射电源查找工具的广泛类型的成功与局限性。 正如预期的那样，大多数查找器在10σ处的完备性水平接近100%，但在5σ时下降到大约10%。可靠性通常在10σ处接近100%，而在较低灵敏度下的性能因查找器而异。所有查找器都表现出完备性与可靠性的典型权衡关系，即在低信噪比下保持高完备性是以降低可靠性为代价的，反之亦然。 最后，我们提出了一系列建议，以改进ASKAPsoft源查找工具的性能。 显示英文摘要

摘要： The Evolutionary Map of the Universe (EMU) is a proposed radio continuum survey of the Southern Hemisphere up to declination +30 deg., with the Australian Square Kilometre Array Pathfinder (ASKAP). EMU will use an automated source identification and measurement approach that is demonstrably optimal, to maximise the reliability, utility and robustness of the resulting radio source catalogues. As part of the process of achieving this aim, a "Data Challenge" has been conducted, providing international teams the opportunity to test a variety of source finders on a set of simulated images. The aim is to quantify the accuracy of existing automated source finding and measurement approaches, and to identify potential limitations. The Challenge attracted nine independent teams, who tested eleven different source finding tools. In addition, the Challenge initiators also tested the current ASKAPsoft source-finding tool to establish how it could benefit from incorporating successful features of the other tools. Here we present the results of the Data Challenge, identifying the successes and limitations for this broad variety of the current generation of radio source finding tools. As expected, most finders demonstrate completeness levels close to 100% at 10sigma dropping to levels around 10% by 5sigma. The reliability is typically close to 100% at 10sigma, with performance to lower sensitivities varying greatly between finders. All finders demonstrate the usual trade-off between completeness and reliability, whereby maintaining a high completeness at low signal-to-noise comes at the expense of reduced reliability, and vice-versa. We conclude with a series of recommendations for improving the performance of the ASKAPsoft source-finding tool.