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arXiv 1111.4286astro-ph.SR
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用SDSS颜色表征矮新星

Characterization of Dwarf Novae Using SDSS Colors

Taichi Kato, Hiroyuki Maehara, Makoto Uemura

摘要:

我们已经开发了一种方法来估计矮新星的轨道周期,从斯隆数字巡天(SDSS)的颜色在静止使用人工神经网络。对于典型的对象以下的周期差距与足够的光度精度,我们能够估计的轨道周期的精度为1西格玛误差为22%。对于轨道周期较长的系统,估计误差更大。我们还开发了一种基于神经网络的分类方法。这种方法已被证明是有效的分类对象分为三类(WZ Sge型,SU UMa型和SS Cyg/Z Cam型),并适用于非常微弱的对象的限制g=21。利用这种方法,我们已经调查了现代瞬态调查(卡塔利娜实时调查)的矮新星的轨道周期的分布。使用由Uemura等人开发的贝叶斯分析(2010,arXiv:1003.0945),我们发现当前样本倾向于给出朝向最短周期的更平坦的分布和周期最小值的更短估计,这可能是由神经网络分析中的不确定性和测光误差引起的。我们还提供了估计的轨道周期,估计分类和补充资料,已知的矮新星与静态SDSS测光。

英文摘要:

We have developed a method for estimating the orbital periods of dwarf novae from the Sloan Digital Sky Survey (SDSS) colors in quiescence using an artificial neural network. For typical objects below the period gap with sufficient photometric accuracy, we were able to estimate the orbital periods with an accuracy to a 1 sigma error of 22 %. The error of estimation is worse for systems with longer orbital periods. We have also developed a neural-network-based method for categorical classification. This method has proven to be efficient in classifying objects into three categories (WZ Sge type, SU UMa type and SS Cyg/Z Cam type) and works for very faint objects to a limit of g=21. Using this method, we have investigated the distribution of the orbital periods of dwarf novae from a modern transient survey (Catalina Real-Time Survey). Using Bayesian analysis developed by Uemura et al. (2010, arXiv:1003.0945), we have found that the present sample tends to give a flatter distribution toward the shortest period and a shorter estimate of the period minimum, which may have resulted from the uncertainties in the neural network analysis and photometric errors. We also provide estimated orbital periods, estimated classifications and supplementary information on known dwarf novae with quiescent SDSS photometry.

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。

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