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Radio Galaxy Zoo: host galaxies and radio morphologies for large surveys from visual inspection

机译:无线电银河动物园:为大型调查提供主机星系和无线电形态   从视觉检查

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摘要

We present early results from Radio Galaxy Zoo, a web-based citizen scienceproject for visual inspection and classification of images from all-sky radiosurveys. The goals of the project are to classify individual radio sources(particularly galaxies with multiple lobes and/or complex morphologies) as wellas matching the continuum radio emission to the host galaxy. Radio images comefrom the FIRST and ATLAS surveys, while matches to potential hosts areperformed with infrared imaging from WISE and SWIRE. The first twelve months ofclassification yielded more than 1 million classifications of more than 60,000sources. For images with at least 75% consensus by the volunteer classifiers,the accuracy is comparable to visual inspection by the expert science team.Based on mid-infrared colors, the hosts associated with radio emission areprimarily a mixture of elliptical galaxies, QSOs, and LIRGs, which are in goodagreement with previous studies. The full catalog of radio lobes and their hostgalaxies will measure the relative populations of host types as a function ofradio morphology and power. Radio Galaxy Zoo has also been an effective methodfor detecting extremely rare objects, such as HyMORs and giant radio galaxies.Results from the project are being used to train automatic algorithms for hostmatching for use in future large continuum surveys such as EMU, as well asestablishing roles for citizen science in projects such as the SKA.
机译:我们介绍了Radio Galaxy Zoo的早期结果,Radio Galaxy Zoo是一个基于网络的公民科学项目,用于对全天空无线电调查的图像进行视觉检查和分类。该项目的目标是对单个无线电源(尤其是具有多个波瓣和/或复杂形态的星系)进行分类,并将连续无线电发射与宿主星系相匹配。无线电图像来自FIRST和ATLAS调查,而与潜在主机的匹配则来自WISE和SWIRE的红外成像。分类的前十二个月产生了超过100万个来源的100万个分类。对于自愿分类者达成至少75%共识的图像,其准确性与专家科学团队的目测相当。基于中红外色,与无线电发射相关的宿主主要是椭圆星系,QSO和LIRG的混合物,与先前的研究相吻合。无线电波瓣及其宿主星系的完整目录将根据无线电形态和功率来测量宿主类型的相对种群。 Radio Zoo Zoo也是检测HyMOR和巨型射电星系等稀有物体的有效方法,该项目的结果被用于训练用于主机匹配的自动算法,以用于未来的大型连续体调查(例如EMU)并确定角色用于SKA等项目中的公民科学。

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    Willett, Kyle W.;

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  • 年度 2016
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