Classification of gamma-ray transients using vision transformer network based on transfer learning
Gamma-ray transient sources include extreme astrophysical phenomena such as gamma-ray bursts(GRBs)and soft gamma-ray repeaters(SGRs).These phenomena are mainly studied using space gamma-ray detectors(such as the GECAM satellite)and represent an important frontier in astronomy research.Additionally,solar activity and complex space environments can generate different types of outbursts(collectively referred to as triggers)in space gamma-ray detectors.Among various trigger events,the rapid classification and identification of GRBs and SGRs are important prerequisites for subsequent physical research.Traditional classification studies often use Bayesian methods to model and calculate the probabilities of different categories using multiple feature information.However,these methods require considerable prior knowledge and reasonable model assumptions,and traditional classification models between different satellites face problems such as difficult migration and narrow applicability.To efficiently and rapidly classify triggers,this paper proposes using the vision transformer network,based on transfer learning and the single-stream multimodality concept,to classify triggers using fewer computational resources and data.We applied this method to GECAM trigger data.Experimental results show that the model achieved an accuracy of 89％in the test set and understood the similarity and distinction between categories.These results indicate that the proposed method has good application prospects for classifying gamma-ray transient sources and other astronomical phenomena.
万方数据
维普
