Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 12, 17 November 2023


Open Access | Article

Potential pulsars prediction based on machine learning

Yuexiang Wu * 1
1 No.2 High School of East China Normal University

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 12, 193-201
Published 17 November 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Yuexiang Wu. Potential pulsars prediction based on machine learning. TNS (2023) Vol. 12: 193-201. DOI: 10.54254/2753-8818/12/20230466.

Abstract

The search for potential pulsars is a difficult job because of the complex nature of the signals and the vast amounts of data involved. In the last few years, a lot of researchers have tried to use machine learning to deal with complex data. This essay examines how machine learning could help to identify potential pulsars, exploring the various types of algorithms and the challenges and limitations associated with this approach. The essay mainly explored three themes: the training of 5 algorithms for the identification of pulsars, the improvement of 2 algorithms by adjusting parameters, and the simplification of the data to improve the processing speed and performance of the algorithms on prediction. All 5 algorithms reached great accuracy after adjustment and the simplification of the input data can help to boost the prediction time and accuracy for future research about pulsars. The essay highlights the need for further research in this area, as machine learning has demonstrated strong potential for pulsar prediction. By analyzing the results of several previous studies, this essay underscores the importance of machine learning as an approach for predicting potential pulsars and made improvements to the performance of current algorithms by adjusting parameters and simplifying the data.

Keywords

pulsars, machine learning, interdisciplinary astronomy (804)

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2023 International Conference on Mathematical Physics and Computational Simulation
ISBN (Print)
978-1-83558-135-3
ISBN (Online)
978-1-83558-136-0
Published Date
17 November 2023
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/12/20230466
Copyright
17 November 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated