Pulsar candidate recognition with deep learning. (January 2019)

• Record Type: Journal Article
• Authors: Zhang, Haoyuan; Zhao, Zhen; An, Tao; Lao, Baoqiang; Chen, Xiao
• Is Part Of: Computers & electrical engineering. Volume 73(2019)
• Page Start: 1
• Page End: 8
• Publication Date: 2019-01
• Subjects: Pulsar candidate classification -- Radio astronomy -- Machine learning -- Methods and techniques -- Convolutional neural network -- Square kilometer array; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.10.016
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In this paper, we present a deep learning-based recognition algorithm to identify pulsars by observing data containing millions of candidates including radio frequency interference and noise sources. The dataset is obtained from the High Time Resolution Universe survey created and updated by the Parkes telescope. We investigate several effective single and combined features via simple logistic regression. To deal with the imbalanced dataset, we oversimplify the original dataset at different sampling rates, which is also one of the learning parameters. After training the pre-processed dataset via a convolutional neural network, we provide a cross-validated evaluation of all candidates. Results show that the deep-learning based recognition algorithm can identify the pulsar and radio frequency interference signals with high accuracy. The precision and recall of radio frequency interference are both 100%, and those of pulsars are 91% and 94%, respectively.