Pulsar detection using machine learning is a challenging problem as it involves extreme class imbalance and strong prioritization of high Recall. This paper is focused on automatic detection of astrophysical pulses in single-pulse searches, both within and across surveys. We use the output from the first stage of our previously developed two-stage Single-Pulse Event Group IDentification approach and focus on the second stage (i.e., classification of pulse candidates). Specifically, for the first time in time-domain single-pulse searches we (1) use boosting and deep learning algorithms for within-survey classification and (2) investigate cross-survey classification by using two transfer learning methods, trAdaBoost (instance-based) and fine-tuning (parameter-based). Our experimental results are based on two benchmark data sets, Green Bank Telescope Drift-scan (GBTDrift) and Pulsar Arecibo L -band Feed Array (PALFA)-extended, created from the GBTDrift survey and the PALFA su...

中文翻译：

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在调查范围内和调查范围内的单脉冲搜索中检测无线电脉冲星

使用机器学习进行脉冲星检测是一个具有挑战性的问题，因为它涉及极端的班级不平衡和对高召回率的强烈优先考虑。本文着重于在单脉冲内和跨调查中自动检测天体物理脉冲。我们使用先前开发的两阶段单脉冲事件组IDentification方法的第一阶段的输出，并专注于第二阶段（即脉冲候选者的分类）。具体而言，在时域单脉冲搜索中，我们第一次（1）使用增强和深度学习算法进行调查内分类，并且（2）通过使用两种转移学习方法trAdaBoost（基于实例）研究交叉调查分类）和微调（基于参数）。我们的实验结果基于两个基准数据集，