Three fast radio bursts monitoring with FAST

Our data set comprises 600 positive samples of observed FRB signals from three sources and 1000 negative samples of noise and radio frequency interference. Furthermore, we provide a machine learning algorithm, Radio Single-Pulse Detection Algorithm Based on Visual Morphological Features (RaSPDAM), with significant improvements in efficiency and accuracy for FRB searches. We also employed the benchmark comparison between conventional single-pulse search software, namely, PRESTO and Heimdall, and RaSPDAM.

RaSPDAMv2 achieves an average precision of 97% and an average recall of 83%, with notable enhancements in computational performance. Future machine learning algorithms can use this as a reference point to measure their performance and identify potential improvements. By enabling more accurate and efficient detection of transient radio events, our work facilitates the FRB and pulsars search pipeline, enhances the potential for discovering new astrophysical phenomena.