Identifying Variable Stars from Kepler Data Using Machine Learning



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Submitted Jul 8, 2021
Published Jul 30, 2021
10.24018/ejphysics.2021.3.4.93

Abstract viewed = 749 times

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Machine learning algorithms play an impressive role in modern technology and address automation problems in many fields as these techniques can be used to identify features with high sensitivity, which humans or other programming techniques aren’t capable of detecting. In addition, the growth of the availability of the data demands the need of faster, accurate, and more reliable automating methods of extracting information, reforming, and preprocessing, and analyzing them in the world of science. The development of machine learning techniques to automate complex manual programs is a time relevant research in astrophysics as it’s a field where, experts are dealing with large sets of data every day. In this study, an automated classification was built for 6 types of star classes Beta Cephei, Delta Scuti, Gamma Doradus, Red Giants, RR Lyrae and RV Tarui with widely varying properties, features extracted from training dataset of stellar light curves obtained from Kepler mission. The Random Forest classification model was used as the Machine Learning model and both periodic and non-periodic features extracted from light curves were used as the inputs to the model. Our implementation achieved an accuracy of 86.5%, an average precision level of 0.86, an average recall value of 0.87, and average F1-Score of 0.86 for the testing dataset obtained from the Kepler mission.

Keywords: Machine Learning Random Forest Short Period Variable Stars

References

  1. L. Breiman, 2001. Random forests. Machine learning, 45(1), Kluwer Academic Publishers, Netherlands, 2001, pp.5-32.
     Google Scholar
  2. L. Breiman, “Bagging predictors. Machine learning, 24(2), Kluwer Academic Publishers. Boston, USA, 1996, pp. 123-140.
     Google Scholar
  3. V. Belokurov, N. W. Evans and Y. Le Du, Light-curve classification in massive variability surveys–II. Transients towards the Large Magellanic Cloud. Monthly Notices of the Royal Astronomical Society, 352(1), pp.233-242, 2004.
     Google Scholar
  4. V. Belokurov, N. W. Evans and Y. L. Du, Light-curve classification in massive variability surveys—I. Microlensing. Monthly Notices of the Royal Astronomical Society, 341(4), pp.1373-1384, 2003.
     Google Scholar
  5. P. G. Willemsen andL. Eyer, A study of supervised classification of Hipparcos variable stars using PCA and Support Vector Machines. arXiv preprint arXiv:0712.2898, 2007.
     Google Scholar
  6. J. Debosscher, L. M. Sarro, C. Aerts, J. Cuypers, B. Vandenbussche, R. Garrido, and E. Solano, Automated supervised classification of variable stars-I. methodology. Astronomy & astrophysics, 475(3), pp.1159-1183, 2007.
     Google Scholar
  7. A. Mahabal, S. G. Djorgovski, M. Turmon, J. Jewell, R. R. Williams, A. J. Drake, M. G. Graham, C. Donalek and E. Glikman, Automated probabilistic classification of transients and variables. Astronomische Nachrichten: Astronomical Notes, 329(3), pp.288-291, 2008.
     Google Scholar
  8. L. M. Sarro, J. Debosscher, M. López and C. Aerts, Automated supervised classification of variable stars-II. Application to the OGLE database. Astronomy & Astrophysics, 494(2), pp.739-768, 2009.
     Google Scholar
  9. J. Blomme, J. Debosscher, J. De Ridder, C. Aerts, R. L. Gilliland, J. Christensen-Dalsgaard, H. Kjeldsen, T. M. Brown, W. J. Borucki, D. Koch, and J. M. Jenkins, Automated classification of variable stars in the asteroseismology program of the Kepler space mission. The Astrophysical Journal Letters, 713(2), p.L204, 2010.
     Google Scholar
  10. M. Paegert, K. G. Stassun and D. M. Burger, The eb factory project. i. a fast, neural-net-based, general purpose light curve classifier optimized for eclipsing binaries. The Astronomical Journal, 148(2), p.31, 2014.
     Google Scholar
  11. P. Dubath, L. Rimoldini, M. Süveges, J. Blomme, M. López, L. M. Sarro, J. De Ridder, J. Cuypers, L. Guy, I. Lecoeur, and K. Nienartowicz, Random forest automated supervised classification of Hipparcos periodic variable stars. Monthly Notices of the Royal Astronomical Society, 414(3), pp.2602-2617, 2011.
     Google Scholar
  12. D. W. Kim, C. A. Bailer-Jones, A package for the automated classification of periodic variable stars. Astronomy & Astrophysics, 587, p.A18, 2016.
     Google Scholar
  13. J. W. Richards, D. L. Starr, N. R. Butler, J. S. Bloom, J. M. Brewer, A. Crellin-Quick, J. Higgins, R. Kennedy and M. Rischard, On machine-learned classification of variable stars with sparse and noisy time-series data. The Astrophysical Journal, 733(1), p.10, 2011.
     Google Scholar
  14. NASA-Kepler/K2 Guest Observation Program. https://keplerscience.arc.nasa.gov/data-products.html#light-curve-files, April 29, 2018.
     Google Scholar
  15. M. Zechmeisterand M. Kürster, The generalised Lomb-Scargle periodogram-a new formalism for the floating-mean and Keplerian periodograms. Astronomy & Astrophysics, 496(2), pp.577-584, 2009.
     Google Scholar
  16. N. R. Lomb, Least-squares frequency analysis of unequally spaced data. Astrophysics and space science, 39(2), pp.447-462, 1976.
     Google Scholar
  17. J. D. Scargle, Studies in astronomical time series analysis. II-Statistical aspects of spectral analysis of unevenly spaced data. The Astrophysical Journal, 263, pp.835-853, 1982.
     Google Scholar
  18. C. E. Ferreira Lopes and N. J. G. Cross, New insights into time series analysis. II-Non-correlated observations. Astronomy and Astrophysics, 604, p.A121, 2017.
     Google Scholar
  19. I. Nun, P. Protopapas, B. Sim, M. Zhu, R. Dave, N. Castro and K. Pichara, Fats: Feature analysis for time series. arXiv preprint arXiv:1506.00010, 2015.
     Google Scholar
  20. J. VanderPlas, A. J. Connolly, Z. Ivezić, and A. Gray, Introduction to astroML: Machine learning for astrophysics. In 2012 conference on intelligent data understanding (pp. 47-54). IEEE, 2012.
     Google Scholar
  21. P. B. Stetson, On the automatic determination of light-curve parameters for cepheid variables. Publications of the Astronomical Society of the Pacific, 108(728), p.851, 1996.
     Google Scholar
  22. D. L. Welch and P. B. Stetson, Robust variable star detection techniques suitable for automated searches-New results for NGC 1866. The Astronomical Journal, 105, pp.1813-1821, 1993.
     Google Scholar