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Bao et al. Complex Eng Syst 2022;2:16 Complex Engineering
DOI: 10.20517/ces.2022.30 Systems

Research Article Open Access

Pulsar identification based on generative adversarial
network and residual network

Zelun Bao, Guiru Liu, Yefan Li, Yanxi Xie, Yang Xu, Zifeng Zhang, Qian Yin, Xin Zheng
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China.

Correspondence to: Dr. Qian Yin, School of Artificial Intelligence, Beijing Normal University, No.19 Xinwai Street, Beijing 100875,
China. E-mail: yinqian@bnu.edu.cn; ORCID: 0000-0002-0354-5490

How to cite this article: Bao Z, Liu G, Li Y, Xie Y, Xu Y, Zhang Z, Yin Q, Zheng X. Pulsar identification based on generative adversarial
network and residual network. Complex Eng Syst 2022;2:16. http://dx.doi.org/10.20517/ces.2022.30

Received: 14 Sep 2022 First Decision: 21 Oct 2022 Revised: 19 Nov 2022 Accepted: 28 Nov 2022 Published: 8 Dec 2022
Academic Editor: Hamid Reza Karimi Copy Editor: Fanglin Lan Production Editor: Fanglin Lan

Abstract
The search for pulsars is an important area of study in modern astronomy. The amount of collected pulsar data is
increasing exponentially as the performance of modern radio telescopes improves, necessitating the improvement
of the original pulsar search methods. Artificial intelligence techniques are currently being used in pulsar candidate
identification tasks. However, improving the accuracy of pulsar candidate identification using artificial intelligence
techniques remains a challenge. Because the amount of collected data is so large, the number of real pulsar sam-
ples is very limited, which leads to a serious sample imbalance problem. Many existing methods ignore this issue,
making it difficult for the model to reach the optimal solution. A framework combining generative adversarial net-
works and residual networks is proposed to greatly alleviate the problem of sample inequality. The framework first
generates stable pulsar images using generative adversarial networks and then designs a deep neural network model
based on residual networks to identify pulsar candidates using intra-block and inter-block residual connectivity. The
ResNet approach has a better ability to fit the data than the CNN approach and can achieve the extraction of features
with more classification ability with a smaller dataset. Meanwhile, the data expanded by the high-quality simulated
samples generated by the generative adversarial network can provide richer identification features and improve the
identification accuracy for pulsar candidates.

Keywords: Pulsar, candidate recognition, artificial intelligence, generative adversarial network, residual network

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, shar-
ing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate
if changes were made.

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