Pulsar candidate recognition with deep learning. (January 2019)
- Record Type:
- Journal Article
- Title:
- Pulsar candidate recognition with deep learning. (January 2019)
- Main Title:
- Pulsar candidate recognition with deep learning
- Authors:
- Zhang, Haoyuan
Zhao, Zhen
An, Tao
Lao, Baoqiang
Chen, Xiao - 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.
- Is Part Of:
- Computers & electrical engineering. Volume 73(2019)
- Journal:
- Computers & electrical engineering
- Issue:
- Volume 73(2019)
- Issue Display:
- Volume 73, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 73
- Issue:
- 2019
- Issue Sort Value:
- 2019-0073-2019-0000
- 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 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00457906/ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.compeleceng.2018.10.016 ↗
- Languages:
- English
- ISSNs:
- 0045-7906
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.680000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9465.xml