Citizen ASAS-SN数据发布I：使用公民科学进行变星分类
Citizen ASAS-SN Data Release I: Variable Star Classification Using Citizen Science
摘要：
我们介绍了Citizen ASAS-SN的第一个结果，这是一个公民科学项目，用于在Zooniverse平台上托管的全天空超新星自动调查（ASAS-SN）。公民ASAS-SN利用更新，更深，更高的节奏ASAS-SN $g$波段数据和任务志愿者分类周期变星候选人的基础上，他们的相位光变曲线。我们从40，640个新的候选变量开始，这些候选变量来自一个${\sim} 7.4$百万颗恒星的输入列表，其中有$δ< -60^\circ$，志愿者们确定了10，420个新发现，他们将其归类为4，234个脉动变量，3，132个旋转变量，2，923个食双星，131个标记为未知的变量。他们对已知的变星进行了分类，脉动变星的准确率为89%，食双星的准确率为81%，旋转变星的准确率为49%。我们检查用户的性能，用户之间的协议，并比较公民科学分类与我们的机器学习分类更新的$g$波段光变曲线。通常，用户活动与更高的分类准确度和更高的用户一致性相关。我们使用用户的“垃圾”分类来开发一个有效的机器学习分类器，以区分真实变量和虚假变量，并且有一个明确的路径来使用这个“垃圾”训练集来显着改进我们的主要机器学习分类器。我们还举例说明了公民ASAS-SN识别异常变量的价值。
英文摘要：
We present the first results from Citizen ASAS-SN, a citizen science project for the All-Sky Automated Survey for Supernovae (ASAS-SN) hosted on the Zooniverse platform. Citizen ASAS-SN utilizes the newer, deeper, higher cadence ASAS-SN $g$-band data and tasks volunteers to classify periodic variable star candidates based on their phased light curves. We started from 40,640 new variable candidates from an input list of ${\sim} 7.4$ million stars with $δ< -60^\circ$ and the volunteers identified 10,420 new discoveries which they classified as 4,234 pulsating variables, 3,132 rotational variables, 2,923 eclipsing binaries, and 131 variables flagged as Unknown. They classified known variable stars with an accuracy of 89% for pulsating variables, 81% for eclipsing binaries, and 49% for rotational variables. We examine user performance, agreement between users, and compare the citizen science classifications with our machine learning classifier updated for the $g$-band light curves. In general, user activity correlates with higher classification accuracy and higher user agreement. We used the user's "Junk" classifications to develop an effective machine learning classifier to separate real from false variables, and there is a clear path for using this "Junk" training set to significantly improve our primary machine learning classifier. We also illustrate the value of Citizen ASAS-SN for identifying unusual variables with several examples.
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。
