Neural networks in the search for fast radio bursts with RATAN-600

Abstract

We present a technique to search for fast radio bursts in records obtained with broadband radiometers having few radio channels. The technique is applied to the RATAN-600 surveys carried out at its Western Sector since the year 2017. A 1D convolutional neural network for multichannel time series classification is developed based on the EfficientNet family of models. The procedure to generate synthetic FRB signals needed for the training dataset is described. We implement a two-stage cascade scheme to effectively suppress the rate of false positive detections. Evaluation of the trained model is provided based on the synthetic events and the giant pulse of the Crab Pulsar.