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Detection of Pulsars Using an Artificial Neural Network

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Emerging Technology in Modelling and Graphics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 937))

Abstract

The research paper demonstrates how to devise an optimal machine learning classifier for the detection of pulsars and then analyzes the performance of various classification models. Pulsars are classified as zero (not a pulsar) or one (pulsar) using logistic regression, decision tree, random forests, KNN classifier, and an artificial neural network. The performance has been analyzed based on five parameters: accuracy, recall, precision, specificity, and prevalence. For this, emission data from the HTRU2 dataset which was collected from the High Time Resolution Universe Survey is being used. Before using any classification algorithms, the patterns in the data were analyzed to understand the correlation between the different characteristics. The classifier proposed in this paper was found to give 98.01% accuracy.

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References

  1. J. Aldrich, R. A. fisher and the making of maximum likelihood 1912–1922. Statist. Sci. 12(3), 162–176 (1997). https://doi.org/10.1214/ss/1030037906

    Article  MathSciNet  MATH  Google Scholar 

  2. N.S. Altman, An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992). https://doi.org/10.1080/00031305.1992.10475879

    Article  MathSciNet  Google Scholar 

  3. S.D. Bates, M. Bailes, B.R. Barsdell, N.D.R. Bhat, M. Burgay, S. Burke-Spolaor, D.J. Champion, P. Coster, N. D’Amico, A. Jameson, S. Johnston, M.J. Keith, M. Kramer, L. Levin, A. Lyne, S. Milia, C. Ng, C. Nietner, A. Possenti, B. Stappers, D. Thornton, W. van Straten, The high time resolution universe pulsar survey VI. An artificial neural network and timing of 75 pulsars. Mon. Not. R. Astron. Soc. 427(2), 1052–1065 (2012). https://doi.org/10.1111/j.1365-2966.2012.22042.x

    Article  Google Scholar 

  4. D.R. Cox, The regression analysis of binary sequences. J. R. Stat. Soc. Ser. B (Methodol.) 20(2), 215–242 (1958). http://www.jstor.org/stable/2983890

    MathSciNet  MATH  Google Scholar 

  5. T. Fawcett, An introduction to roc analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006). https://doi.org/10.1016/j.patrec.2005.10.010, http://www.sciencedirect.com/science/article/pii/S016786550500303X (ROC Analysis in Pattern Recognition)

    Article  MathSciNet  Google Scholar 

  6. R.H.R. Hahnloser, R. Sarpeshkar, M.A. Mahowald, R.J. Douglas, H.S. Seung, Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405, 947–951 (2000). https://doi.org/10.1038/35016072

    Article  Google Scholar 

  7. T.K. Ho, Random decision forests, in Proceedings of 3rd International Conference on Document Analysis and Recognition, vol 1 (1995), pp. 278–282 https://doi.org/10.1109/ICDAR.1995.598994

  8. 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. Mon. Not. R. Astron. Soc. 459(1), 1104–1123 (2016). https://doi.org/10.1093/mnras/stw656

    Article  Google Scholar 

  9. W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943). https://doi.org/10.1007/BF02478259

    Article  MathSciNet  MATH  Google Scholar 

  10. V. Morello, E.D. Barr, M. Bailes, C.M. Flynn, E.F. Keane, W. van Straten, Spinn: a straightforward machine learning solution to the pulsar candidate selection problem. Mon. Not. R. Astron. Soc. 443(2), 1651–1662 (2014). https://doi.org/10.1093/mnras/stu1188

    Article  Google Scholar 

  11. A. Punia, A. Sardana, M. Subashini, Evaluating advanced machine learning techniques for pulsar detection from htru survey, in 2017 International Conference on Intelligent Sustainable Systems (ICISS) (2017), pp. 470–474. https://doi.org/10.1109/ISS1.2017.8389455

  12. J.R. Quinlan, Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986). https://doi.org/10.1007/BF00116251

    Article  Google Scholar 

  13. P. Verhulst, Notice sur la loi que la population suit dans son accroissement. Corresp. Math. Phys. 10, 113–126 (1838). https://ci.nii.ac.jp/naid/10015246307/en/

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Authors and Affiliations

  1. Institute of Engineering & Management, Salt Lake Electronics Complex, Gurukul, Y-12, Sector V, Kolkata, 700091, India

    Rajarshi Lahiri, Souvik Dey, Soumit Roy & Soumyadip Nag

Authors
  1. Rajarshi Lahiri
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  2. Souvik Dey
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  3. Soumit Roy
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  4. Soumyadip Nag
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Corresponding author

Correspondence to Rajarshi Lahiri .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Jyotsna Kumar Mandal

  2. Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, West Bengal, India

    Debika Bhattacharya

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Lahiri, R., Dey, S., Roy, S., Nag, S. (2020). Detection of Pulsars Using an Artificial Neural Network. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_15

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  • DOI: https://doi.org/10.1007/978-981-13-7403-6_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7402-9

  • Online ISBN: 978-981-13-7403-6

  • eBook Packages: EngineeringEngineering (R0)

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