Pulsar Candidate Classification Using a Computer Vision Method from a Combination of  Convolution and Attention

Abstract 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, implements a multilayer perceptron to score one-dimensional features, and relies on logistic regression to judge the corresponding scores. In the data preprocessing stage, we perform 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 (FPR) and 98.85% accuracy in our GPPS test set.