Selection of radio pulsar candidates using artificial neural networks

TL;DR

This paper introduces an artificial neural network method to automatically identify credible radio pulsar candidates from large survey data, significantly aiding in the discovery process.

Contribution

The paper presents a novel neural network-based technique for pulsar candidate selection, improving efficiency and accuracy over manual inspection methods.

Findings

Successfully identified a new pulsar in survey data

Demonstrated the neural network's effectiveness in candidate classification

Reduced manual effort in pulsar candidate analysis

Abstract

Radio pulsar surveys are producing many more pulsar candidates than can be inspected by human experts in a practical length of time. Here we present a technique to automatically identify credible pulsar candidates from pulsar surveys using an artificial neural network. The technique has been applied to candidates from a recent re-analysis of the Parkes multi-beam pulsar survey resulting in the discovery of a previously unidentified pulsar.