TSARDI：一种用于过境系外行星光变曲线的机器学习数据拒绝算法
TSARDI: a Machine Learning data rejection algorithm for transiting exoplanet light curves
摘要：
我们提出了TSARDI，一个有效的拒绝算法，旨在提高过境检测效率的大规模调查收集的数据。TSARDI基于机器学习聚类算法DBSCAN，其目的是作为一个强大的和自适应的过滤器，旨在识别数据去趋势过程中留下的不需要的噪声点。TSARDI是一种无监督的方法，它可以单独处理每个光变曲线;不需要任何其他场光变曲线的先前知识。我们进行了模拟过境搜索注入行星的QES项目获得的真实数据，并表明TSARDI导致整体过境检测效率增加了$\sim$ 11\%，从相同的样本，但使用标准的sigma剪辑算法获得的结果相比。对于我们样本的较亮端（主星星等< 12），TSARDI实现了$\sim$ 80%的注入行星的检测效率。虽然我们的算法主要是为系外行星领域开发的，但它很容易适应和扩展，可用于任何时间序列。
英文摘要：
We present TSARDI, an efficient rejection algorithm designed to improve the transit detection efficiency in data collected by large scale surveys. TSARDI is based on the Machine Learning clustering algorithm DBSCAN, and its purpose is to serve as a robust and adaptable filter aiming to identify unwanted noise points left over from data detrending processes. TSARDI is an unsupervised method, which can treat each light curve individually; there is no need of previous knowledge of any other field light curves. We conduct a simulated transit search by injecting planets on real data obtained by the QES project and show that TSARDI leads to an overall transit detection efficiency increase of $\sim$11\%, compared to results obtained from the same sample, but using a standard sigma-clip algorithm. For the brighter end of our sample (host star magnitude < 12), TSARDI achieves a detection efficiency of $\sim$80\% of injected planets. While our algorithm has been developed primarily for the field of exoplanets, it is easily adaptable and extendable for use in any time series.
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