Advances in Pulsar Candidate Selection: A Neural Network Perspective

Abstract

Pulsars have played an important role in comprehending the universe. They play a key role in understanding various phenomena like general relativity, gravitational waves, properties of matter, collision of black holes and the evolution of stars and nebulae. Thus, identifying them is a crucial task. The increasing number of surveys has created a large volume of candidate samples, in the range of several million. Hence, it is impossible to select pulsars from these samples using human-driven methods. Automatic Pulsar Candidate Identification (APCI) was introduced for this purpose. In recent years, various deep-learning techniques and models have been implemented for this purpose. Specific deep neural network models and hybrid models were designed to select pulsar candidates from various surveys consisting of radio and X-ray samples. In this study, a series of models implementing ANN, CNN and GNN are discussed capable of selecting pulsar candidates. These models were trained using a wide range of surveys.