A Review of Fast Radio Burst Search Methods Based on Machine Learning

Fast Radio Bursts (FRBs) are a hot topic in the field of astronomy at present. Its related research was also selected by the journal Nature as one of the top 10 scientific discoveries of 2020. The characteristics that FRBs are millisecond-duration and rarely repeated make them hard to be captured. Identifying FRBs from massive astronomical observation data by human review is a time-consuming and laborious task. With the rapid development of machine learning technology, it is possible to carry out a real-time search and multi-frequency tracking for FRB events. This paper analyzes and summarizes the existing representative results from two aspects: traditional machine learning method and deep learning method. Finally, the existing problems and challenges of FRB search technology based on machine learning are discussed, and future development trend is also analyzed. In the near future, deep learning technology will be more widely used and become a powerful tool to search for FRBs efficiently.