公交检测的BAST算法
The BAST algorithm for transit detection
摘要：
用于测光系外行星搜索的先驱太空任务，CoRoT，稳定地监测每个视野中的大约12000颗恒星。凌日探测算法被用于获得有希望的行星候选者，然后通过地面观测进行跟踪。我们提出了BAST（柏林自动搜索过境），这是一种用于周期性过境检测的新算法，并在模拟的CoRoT数据上对其进行了测试。BAST在标准化、过滤、可变性拟合和展开光变曲线中搜索盒形信号。应用低通滤波器来去除高频信号，并减去数据分段的线性拟合以去除恒星的变化性。然后，在高于给定检测阈值识别的运输事件中执行对周期性的搜索。定义了一些标准，以更好地将行星候选者与双星分开。从模拟的CoRoT光变曲线的分析，我们表明，BAST检测性能是类似的盒拟合最小二乘（BLS）方法，如果信噪比是高的。但是，BAST框搜索凌日的计算速度比BLS方法快10倍。通过添加周期性的过渡到模拟的CoRoT数据，我们表明，最小的周期性的深度与BAST检测是一个线性增加的函数的噪声水平。对于低噪声光变曲线，探测极限对应于凌日深度d~ 0.01%，即一颗行星围绕一颗太阳型恒星的地球半径。
英文摘要：
The pioneer space mission for photometric exoplanet searches, CoRoT, steadily monitors about 12000 stars in each of its fields of view. Transit detection algorithms are applied to derive promising planetary candidates, which are then followed-up with ground-based observations. We present BAST (Berlin Automatic Search for Transits), a new algorithm for periodic transit detection, and test it on simulated CoRoT data. BAST searches for box-shaped signals in normalized, filtered, variability-fitted, and unfolded light curves. A low-pass filter is applied to remove high-frequency signals, and linear fits to subsections of data are subtracted to remove the star's variability. A search for periodicity is then performed in transit events identified above a given detection threshold. Some criteria are defined to better separate planet candidates from binary stars. From the analysis of simulated CoRoT light curves, we show that the BAST detection performance is similar to that of the Box-fitting Least-Square (BLS) method if the signal-to-noise ratio is high. However, the BAST box search for transits computes 10 times faster than the BLS method. By adding periodic transits to simulated CoRoT data, we show that the minimum periodic depth detectable with BAST is a linearly increasing function of the noise level. For low-noise light curves, the detection limit corresponds to a transit depth d~0.01%, i.e. a planet of 1 Earth radius around a solar-type star.
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