2023/04/23 by NanNan Cai, J. L. Han, Cai, NanNan +9
Computer Science · #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Seismology and Earthquake Studies
paper · pdf · doi:10.48550/arxiv.2304.11604
openalex publication_date 2023/04/23 · openalex created_date 2023/04/27 · openalex updated_date 2026/07/28
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.