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Pulsar Candidate Classification Using A Computer Vision Method Combining with Convolution and Attention
NanNan Cai, JinLin Han, WeiCong Jing, ZeKai Zhang, DeJiang Zhou, Xue Chen
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Artificial intelligence methods are indispensable to identifying pulsars from large amounts of candidates. We develop a new pulsar identification system that utilizes the CoAtNet to score two-dimensional features of candidates, uses a multilayer perceptron to score one-dimensional features, and uses logistic regression to judge the scores above. In the data preprocessing stage, we performed two feature fusions separately, one for one-dimensional features and the other for two-dimensional features, which are used as inputs for the multilayer perceptron and the CoAtNet respectively. The newly developed system achieves 98.77\% recall, 1.07\% false positive rate and 98.85\% accuracy in our GPPS test set.
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