# Pulsar Candidate Selection Using Ensemble Networks for FAST Drift-Scan Survey

## TL;DR

This paper introduces an ensemble neural network system for efficient pulsar candidate classification in FAST drift-scan surveys, significantly improving speed and accuracy over previous methods.

## Contribution

It develops a residual network-based ensemble classifier tailored for FAST data, enhancing real-time pulsar candidate sorting and providing publicly available labeled data.

## Key findings

- Achieves >96% recall of real pulsars in top 1% candidates
- Classifies over 1.6 million candidates daily with dual-GPU setup
- Facilitates real-time processing of pulsar search data

## Abstract

The Commensal Radio Astronomy Five-hundred-meter Aperture Spherical radio Telescope (FAST) Survey (CRAFTS) utilizes the novel drift-scan commensal survey mode of FAST and can generate billions of pulsar candidate signals. The human experts are not likely to thoroughly examine these signals, and various machine sorting methods are used to aid the classification of the FAST candidates. In this study, we propose a new ensemble classification system for pulsar candidates. This system denotes the further development of the pulsar image-based classification system (PICS), which was used in the Arecibo Telescope pulsar survey, and has been retrained and customized for the FAST drift-scan survey. In this study, we designed a residual network model comprising 15 layers to replace the convolutional neural networks (CNNs) in PICS. The results of this study demonstrate that the new model can sort >96% of real pulsars to belong the top 1% of all candidates and classify >1.6 million candidates per day using a dual--GPU and 24--core computer. This increased speed and efficiency can help to facilitate real-time or quasi-real-time processing of the pulsar-search data stream obtained from CRAFTS. In addition, we have published the labeled FAST data used in this study online, which can aid in the development of new deep learning techniques for performing pulsar searches.