分散无线电脉冲的检测：候选识别和分类的机器学习方法
Detection of Dispersed Radio Pulses: A machine learning approach to candidate identification and classification
摘要：
在天文数据集中搜寻地外瞬时信号是当前研究的一个活跃领域。然而，在有关单脉冲检测的文献中缺乏机器学习技术。本文提出了一种新的，两个阶段的方法来识别和分类分散脉冲群（DPG）在单脉冲搜索输出。第一阶段识别DPG并提取特征以使用新的峰值识别算法来表征它们，所述峰值识别算法跟踪信噪比与分散度量的图中的局部最大值周围的倾斜趋势。第二阶段使用监督机器学习对DPG进行分类。我们创建了四个基准数据集：一个不平衡版本和三个平衡版本，使用三种不同的不平衡处理。我们通过在四个基准版本中的每一个上训练和测试六种机器学习算法的二进制和多类版本，对48个分类器进行了经验评估。虽然每个分类器都有优点和缺点，但无论使用哪种机器学习算法，所有具有不平衡处理的分类器都比具有不平衡数据的分类器具有更高的召回值。基于基准测试结果，我们选择了一个分类器子集，对绿岸望远镜42，405次观测中确定的超过150万个DPG的完整未标记数据集进行分类。总的来说，使用多类集成树学习器结合两种过采样不平衡处理的分类器是最有效的;它们识别了基准数据集中没有的其他已知分类器，并提供了六个潜在的发现，误报率明显低于其他分类器。
英文摘要：
Searching for extraterrestrial, transient signals in astronomical data sets is an active area of current research. However, machine learning techniques are lacking in the literature concerning single-pulse detection. This paper presents a new, two-stage approach for identifying and classifying dispersed pulse groups (DPGs) in single-pulse search output. The first stage identified DPGs and extracted features to characterize them using a new peak identification algorithm which tracks sloping tendencies around local maxima in plots of signal-to-noise ratio vs. dispersion measure. The second stage used supervised machine learning to classify DPGs. We created four benchmark data sets: one unbalanced and three balanced versions using three different imbalance treatments.We empirically evaluated 48 classifiers by training and testing binary and multiclass versions of six machine learning algorithms on each of the four benchmark versions. While each classifier had advantages and disadvantages, all classifiers with imbalance treatments had higher recall values than those with unbalanced data, regardless of the machine learning algorithm used. Based on the benchmarking results, we selected a subset of classifiers to classify the full, unlabelled data set of over 1.5 million DPGs identified in 42,405 observations made by the Green Bank Telescope. Overall, the classifiers using a multiclass ensemble tree learner in combination with two oversampling imbalance treatments were the most efficient; they identified additional known pulsars not in the benchmark data set and provided six potential discoveries, with significantly less false positives than the other classifiers.
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