论文标题
使用明显的复杂度度量检测大型调查中的复杂来源
Detecting complex sources in large surveys using an apparent complexity measure
论文作者
论文摘要
大型天文学调查几乎可以肯定包含以前从未见过的类型的新物体。算法对“未知未知数”的检测是一个困难的问题，因为与机器相比，不寻常的事物通常更容易发现。我们使用以前用于检测多组分无线电源的表观复杂性的概念，以完全自动化和盲目的方式扫描宇宙的无线电连续性进化图（EMU）试点调查数据。在这里，我们描述了如何定义和测量复杂性，如何将其应用于试点调查数据，以及我们如何使用拥挤的“动物园”校准这些有趣对象的完整性和纯度。该结果还与人类检查发现的EMU试点调查（包括奇数无线电圈）中已经检测到的意外和异常来源进行了比较。
Large area astronomical surveys will almost certainly contain new objects of a type that have never been seen before. The detection of 'unknown unknowns' by an algorithm is a difficult problem to solve, as unusual things are often easier for a human to spot than a machine. We use the concept of apparent complexity, previously applied to detect multi-component radio sources, to scan the radio continuum Evolutionary Map of the Universe (EMU) Pilot Survey data for complex and interesting objects in a fully automated and blind manner. Here we describe how the complexity is defined and measured, how we applied it to the Pilot Survey data, and how we calibrated the completeness and purity of these interesting objects using a crowd-sourced 'zoo'. The results are also compared to unexpected and unusual sources already detected in the EMU Pilot Survey, including Odd Radio Circles, that were found by human inspection.
