Application Research of Pre-Trained Models in Pulsar Candidate Selection
Pre-trained models have gradually become a tool and are applied to various downstream tasks.Among them,due to the different positions of each telescope,the use of digital backend,processing technology,and the surrounding interference environment,pulsar candidate selection requires training from scratch using extremely imbalanced data and designing deep learning models,which poses problems such as complex processes,poor timeliness,and high difficulty.In response to the above issues,a pre-trained model based on the ImageNet dataset was compared and analyzed for its performance on the pulsar dataset,including Precision,Recall,and F1 Score.Firstly,evaluate the transferability of each model on the pulsar dataset using the LogME method.Secondly,using backend fusion to fuse the two features of frequency phase and time phase maps.Finally,use a layer by layer thawing method to fine-tune the 14 models with higher transferability scores.The results show that the pre-trained method requires fewer resources and has a fast convergence speed,which can be applied to pulsar screening tasks.When further improving the model performance,these models can also be considered as basic modules for feature extraction.
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