论文标题

搜索具有Zwicky Transient设施的定期变化的Quasar候选者:结果和含义

Searching for quasar candidates with periodic variations from the Zwicky Transient Facility: results and implications

论文作者

Chen, Yong-Jie, Zhai, Shuo, Liu, Jun-Rong, Guo, Wei-Jian, Peng, Yue-Chang, Li, Yan-Rong, SongSheng, Yu-Yang, Du, Pu, Hu, Chen, Wang, Jian-Min

论文摘要

我们通过与斯隆数字天空调查的类星体目录和​​v {é} ron-cetty \&v {é} ron进行交叉匹配,对Zwicky瞬态设施的档案光度数据进行了定期变化进行系统搜索。我们首先使用通用的伦敦量表周期图和自动相关函数选择184个原始的周期性候选者,然后根据两个红色噪声模型,即抑制随机步行(DRW)和单个Power-Law(SPL)模型来估算其周期性的统计学意义。因此,我们最终确定了106(DRW)和86个(SPL)候选者,其周期性最显着,在143,700种类星体中发生了最大的变化。我们进一步使用贝叶斯因子比较了DRW和SPL模型,这表明SPL模型对原始样品的相对偏好。因此,我们采用SPL确定为最终样本的候选者并总结了其基本属性。我们通过提供其他档案调查数据来验证其周期性来扩展选定候选人的光曲线。但是,只有三个候选人(有6-8个周期)符合选择标准。该结果清楚地表明,该变异性必须是准周期性或由随机的红色噪声引起的,而不是严格周期性。这对现有的搜索方法构成了挑战,并呼吁开发新的有效方法。

We conduct a systematic search for quasars with periodic variations from the archival photometric data of the Zwicky Transient Facility by cross-matching with the quasar catalogs of the Sloan Digital Sky Survey and V{é}ron-Cetty \& V{é}ron. We first select out 184 primitive periodic candidates using the generalized Lomb-Scargle periodogram and auto-correlation function and then estimate their statistical significance of periodicity based on two red-noise models, i.e., damped random walk (DRW) and single power-law (SPL) models. As such, we finally identify 106 (DRW) and 86 (SPL) candidates with the most significant periodic variations out of 143,700 quasars. We further compare DRW and SPL models using Bayes factors, which indicate a relative preference of the SPL model for our primitive sample. We thus adopt the candidates identified with SPL as the final sample and summarize its basic properties. We extend the light curves of the selected candidates by supplying other archival survey data to verify their periodicity. However, only three candidates (with 6-8 cycles of periods) meet the selection criteria. This result clearly implies that, instead of being strictly periodic, the variability must be quasi-periodic or caused by stochastic red-noise. This exerts a challenge to the existing search approaches and calls for developing new effective methods.

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