Pulsar Star Detection: A Comparative Analysis of Classification Algorithms using SMOTE

Authors

  • Apratim Sadhu Department of Computer Science Engineering, Chandigarh University, Mohali, India

DOI:

https://doi.org/10.24203/ijcit.v11i1.193

Keywords:

Accuracy, Algorithms, Classification, Machine Learning, Pulsars, SMOTE

Abstract

A Pulsar is a highly magnetized rotating compact star whose magnetic poles emit beams of radiation. The application of pulsar stars has a great application in the field of astronomical study. Applications like the existence of gravitational radiation can be indirectly confirmed from the observation of pulsars in a binary neutron star system. Therefore, the identification of pulsars is necessary for the study of gravitational waves and general relativity. Detection of pulsars in the universe can help research in the field of astrophysics. At present, there are millions of pulsar candidates present to be searched. Machine learning techniques can help detect pulsars from such a large number of candidates. The paper discusses nine common classification algorithms for the prediction of pulsar stars and then compares their performances using various classification metrics such as classification accuracy, precision and recall value, ROC score and f-score on both balanced and unbalanced data. SMOTE-technique is used to balance the data for better results. Among the nine algorithms, XGBoosting algorithm achieved the best results. The paper is concluded with prospects of machine learning for pulsar detection in the field of astronomy.

References

https://imagine.gsfc.nasa.gov/science/objects/neutron_stars1.html

https://d1b10bmlvqabco.cloudfront.net/attach/jz8smbptoj35ra/i8xgc5x4yhoyo/k0fny104qt72/project.pdf

Cheng Jun Zhang, Zhen Hong Shang, Wan Min Chen, Liu Xie, Xiang Hua Miao. (2020). A Review of Research on Pulsar Candidate Recognition Based on Machine Learning. Procedia Computer Science, Volume 166, 2020, Pages 534-538, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.02.050.

https://www.irjet.net/archives/V7/i6/IRJET-V7I61195.pdf

Singh, Amitesh & Pathak, Kamlesh. (2020). A machine learning-based approach towards the improvement of SNR of pulsar signals.

https://imagine.gsfc.nasa.gov/science/objects/neutron_stars1.html

http://www.scienceguyrob.com/wpcontent/uploads/2016/12/WhyArePulsarsHardToFind_Lyon_2016.pdf

Vapnik, V. N. (1998). Statistical Learning Theory. Wiley-Interscience.

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning : with Applications in R. New York :Springer, 2013.

R. J. Lyon, B. W. Stappers, S. Cooper, J. M. Brooke, J. D. Knowles, Fifty Years of Pulsar Candidate Selection: From simple filters to a new principled real-time classification approach MNRAS, 2016.

https://archive.ics.uci.edu/ml/datasets/HTRU2

R. J. Lyon, "Why Are Pulsars Hard To Find?", PhD Thesis, University of Manchester, 2015.

R. J. Lyon, "PulsarFeatureLab", 2015, https://dx.doi.org/10.6084/m9.figshare.1536472.v1.

https://scikit-learn.org/stable/supervised_learning.html#supervised-learning

https://keras.io/api/models/sequential/

Kingma, Diederik & Ba, Jimmy. (2014). Adam: A Method for Stochastic Optimization. International Co ference on Learning Representations.

https://keras.io/api/optimizers/adam/

https://keras.io/api/losses/probabilistic_losses/#binarycrossentropy-class

https://github.com/apratimsadhu01/Pulsar-Star-Detection

Chawla, Nitesh & Bowyer, Kevin & Hall, Lawrence & Kegelmeyer, W.. (2002). SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. (JAIR). 16. 321-357. 10.1613/jair.953.

Downloads

Published

2022-03-05

How to Cite

Sadhu, A. (2022). Pulsar Star Detection: A Comparative Analysis of Classification Algorithms using SMOTE. International Journal of Computer and Information Technology(2279-0764), 11(1). https://doi.org/10.24203/ijcit.v11i1.193