Searching for Anomalies in the ZTF Catalog of Periodic Variable Stars
Searching for Anomalies in the ZTF Catalog of Periodic Variable Stars
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DOI：
10.3847/1538-4357/ac69d4
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Ho Sang Chan;V. Villar;Siu-Hei Cheung;S. Ho;A. O’Grady;M. Drout;M. Renzo
中科院分区：
文献类型：
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作者：
Ho Sang Chan;V. Villar;Siu-Hei Cheung;S. Ho;A. O’Grady;M. Drout;M. Renzo
周期变量照亮了恒星一生中的物理过程。广域观测继续增加我们发现周期变星的几率。自动化方法对于识别有趣的周期变星以进行多波长和光谱跟踪是必不可少的。在这里，我们提出了一种新的无监督机器学习方法来寻找反常周期变量，使用Chen等人的Zwky瞬变设施周期变星星表中的相位折叠光曲线。我们使用卷积变分自动编码器来学习低维的潜在表示，并通过隔离森林在该潜在维度内搜索异常。我们识别具有不规则变异性的异常。大多数顶部异常可能是高度可变的红巨星或集中在银河系盘中的渐近巨型分支恒星；已识别的异常中的一小部分与年轻恒星对象更一致。鼓励进行详细的光谱后续观测，以揭示这些异常的性质。
Periodic variables illuminate the physical processes of stars throughout their lifetime. Wide-field surveys continue to increase our discovery rates of periodic variable stars. Automated approaches are essential to identify interesting periodic variable stars for multiwavelength and spectroscopic follow-up. Here we present a novel unsupervised machine-learning approach to hunt for anomalous periodic variables using phase-folded light curves presented in the Zwicky Transient Facility Catalogue of Periodic Variable Stars by Chen et al. We use a convolutional variational autoencoder to learn a low-dimensional latent representation, and we search for anomalies within this latent dimension via an isolation forest. We identify anomalies with irregular variability. Most of the top anomalies are likely highly variable red giants or asymptotic giant branch stars concentrated in the Milky Way galactic disk; a fraction of the identified anomalies are more consistent with young stellar objects. Detailed spectroscopic follow-up observations are encouraged to reveal the nature of these anomalies.