趋势过滤算法在MACHO数据库中的应用
Application of the Trend Filtering Algorithm on the MACHO Database
摘要：
由于变星观测中系统学/趋势的强烈影响，我们采用趋势过滤算法（TFA）的一个子集上的CASTO数据库和搜索变星。TFA已经成功地应用于行星过境搜索，其中在存在噪声和各种系统学的情况下寻找弱的、持续时间短的周期性调光（由于，例如，不完美的平场、拥挤等）。这些后一种效应在光度时间序列中引入有色噪声，这可能导致信号的完全缺失。通过使用大量的可用光度时间序列的一个给定的领域，TFA利用的事实，即相同类型的系统出现在几个/许多时间序列的同一领域。因此，我们适合每个目标时间序列的（最小二乘意义）最佳线性组合的模板和频率分析的残差。一旦发现一个信号，我们重建的信号，采用完整的模型，包括信号，系统和噪声。我们将TFA应用于来自每个CNOO大麦云云场#1和#79的子集的最亮的~5300个对象。我们发现，对原始数据进行的傅立叶频率分析检测到约60%的对象为趋势主导。使用TFA后，该数字基本上降至零。总共，我们检测到387个变量在这两个领域，其中183个将保持未检测到没有使用TFA。在可能的情况下，我们对发现的变量进行初步分类。
英文摘要：
Due to the strong effect of systematics/trends in variable star observations, we employ the Trend Filtering Algorithm (TFA) on a subset of the MACHO database and search for variable stars. TFA has been applied successfully in planetary transit searches, where weak, short-lasting periodic dimmings are sought in the presence of noise and various systematics (due to, e.g., imperfect flat fielding, crowding, etc). These latter effects introduce colored noise in the photometric time series that can lead to a complete miss of the signal. By using a large number of available photometric time series of a given field, TFA utilizes the fact that the same types of systematics appear in several/many time series of the same field. As a result, we fit each target time series by a (least-square-sense) optimum linear combination of templates and frequency-analyze the residuals. Once a signal is found, we reconstruct the signal by employing the full model, including the signal, systematics and noise. We apply TFA on the brightest ~5300 objects from subsets of each of the MACHO Large Magellanic Cloud fields #1 and #79. We find that the Fourier frequency analysis performed on the original data detect some 60% of the objects as trend-dominated. This figure decreases essentially to zero after using TFA. Altogether, We detect 387 variables in the two fields, 183 of which would have remained undetected without using TFA. Where possible, we give preliminary classification of the variables found.
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