Abstract
The discovery of planets outside our Solar System, called exoplanets, allows us to study the feasibility of life outside Earth. Different techniques such as the transit method have been employed to detect and identify exoplanets. The amount of time and effort required to perform such a task, hinder the manual examination of the existing data. Several machine learning approaches have been proposed to deal with this matter, though they are not yet unerring. Therefore, new models continue to be proposed. In this work, we present experimental results using the K-Nearest Neighbors, Random Forests, Convolutional Neural Network and the Ridge classifier models to identify simulated transit signals. Furthermore, we propose a methodology based on the Empirical Mode Decomposition and Ensemble Empirical Mode Decomposition techniques for light curve preprocessing. Following this methodology we prove that multiresolution analysis can be used to improve the robustness of the presented models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
BATMAN Python package https://www.cfa.harvard.edu/~lkreidberg/batman/.
- 2.
- 3.
EEMD Matlab function https://github.com/ron1818/PhD_code/tree/master/EMD_EEMD.
References
Borucki, W.J., et al.: Kepler planet-detection mission: introduction and first results. Science 327, 977–980 (2010)
Basri, G., Borucki, W.J., Koch, D.: The Kepler mission: a wide-field transit search for terrestrial planets. New Astron. Rev. 49, 478–485 (2005)
Auvergne, M., et al.: The CoRoT satellite in flight: description and performance. Astron. Astrophys. 506, 411–424 (2009)
Ricker, G.R., et al.: Transiting exoplanet survey satellite (TESS). J. Astron. Telescop. Instrum. Syst. 1, 014003 (2015)
Thompson, S.E., et al.: A machine learning technique to identify transit shaped signals. Astrophys. J. 812, 46 (2015)
Catanzarite, J.H.: Autovetter planet candidate catalog for Q1–Q17 data release 24. Astronomy & Astrophysics (2015)
Coughlin, J.L., et al.: Planetary candidates observed by Kepler. VII. the first fully uniform catalog based on the entire 48-month data set (Q1–Q17 DR24). Astrophys. J. Supplement Ser. 224, 12 (2016)
Armstrong, D., et al.: Automatic vetting of planet candidates from ground-based surveys: machine learning with NGTS. Monthly Not. Roy. Astron. Soc. 478, 4225–4237 (2018)
Schanche, N., et al.: Machine-learning approaches to exoplanet transit detection and candidate validation in wide-field ground-based surveys. Monthly Not. Roy. Astron. Soc. 483, 5534–5547 (2019)
Dattilo, A., et al.: Identifying exoplanets with deep learning. II. Two new super-earths uncovered by a neural network in K2 data. Astron. J. 157, 169 (2019)
Shallue, C.J., Vanderburg, A.: Identifying exoplanets with deep learning: a five-planet resonant chain around Kepler-80 and an eighth planet around Kepler-90. Astron. J. 155, 94 (2018)
Ansdell, M., et al.: Scientific domain knowledge improves exoplanet transit classification with deep learning. Astrophys. J. 869, L7 (2018)
Yu, L., et al.: Identifying exoplanets with deep learning. III. Automated triage and vetting of TESS candidates. Astron. J. 158, 25 (2019)
Graps, A.: An introduction to wavelets. IEEE Comput. Sci. Eng. 2, 50–61 (1995)
Bravo, J.P., Roque, S., Estrela, R., Leão, I.C., De Medeiros, J.R.: Wavelets: a powerful tool for studying rotation, activity, and pulsation in Kepler and CoRoT stellar light curves. Astron. Astrophys. 568, A34 (2014)
Jara-Maldonado, M., Alarcon-Aquino, V., Rosas-Romero, R., Starostenko, O., Ramirez-Cortes, J.M.: Transiting exoplanet discovery using machine learning techniques: a survey. Earth Sci. Inf. 13(3), 573–600 (2020). https://doi.org/10.1007/s12145-020-00464-7
Zeiler, A., et al.: Empirical mode decomposition - an introduction. In: Proceedings of the International Joint Conference on Neural Networks, pp. 1–8 (2010)
Mandic, D.P., ur Rehman, N., Wu, Z., Huang, N.E.: Empirical Mode Decomposition-based time-frequency analysis of multivariate signals: the power of adaptive data analysis. IEEE Sig. Process. Mag. 30, 74–86 (2013)
Huang, N.E., Wu, Z.: A review on Hilbert-Huang transform: method and its applications to geophysical studies. Rev. Geophys. 46, 1–23 (2008)
Fang, K., et al.: Comparison of EMD and EEMD in rolling bearing fault signal analysis. In: 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–5 (2018)
Fontugne, R., Borgnat, P., Flandrin, P.: Online empirical mode decomposition. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4306–4310 (2017)
Torres, M.E., Colominas, M.A., Schlotthauer, G., Flandrin, P.: A complete ensemble empirical mode decomposition with adaptive noise. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4144–4147 (2011)
Daubechies, I., Lu, J., Wu, H.T.: Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal. 30, 243–261 (2011)
Pearson, K.A., Palafox, L., Griffith, C.A.: Searching for exoplanets using artificial intelligence. Monthly Not. Roy. Astron. Soc. 474, 478–491 (2018)
Tingley, B.: Improvements to existing transit detection algorithms and their comparison. Astron. Astrophys. 408, L5–L7 (2003)
Mucherino, A., Papajorgji, P.J., Pardalos, P.M.: In: k-Nearest Neighbor Classification, pp. 83–106. Springer, New York (2009)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, vol. 4, 4th edn. Academic Press Inc., USA (2008)
Haykin, S.S.: Neural Networks and Learning Machines, 3rd edn. Pearson Education, Upper Saddle River (2009)
Kreidberg, L.: batman: BAsic transit model cAlculatioN in python. Publ. Astron. Soc. Pacific 127, 1161–1165 (2015)
Mandel, K., Agol, E.: Analytic light curves for planetary transit searches. Astrophys. J. 580, L171–L175 (2002)
Committee, E.S.: Statistical significance and biological relevance. EFSA J. 9, 2372 (2011)
Rauer, H., et al.: The PLATO 2.0 mission. Exp. Astron. 38, 249–330 (2014)
Acknowledgments
The authors would like to thank the Mexican National Council on Science and Technology (CONACyT) and the Universidad de las Americas Puebla (UDLAP) for their support through the doctoral scholarship program. This paper includes data collected by the Kepler mission and obtained from the MAST data archive at the Space Telescope Science Institute (STScI). Funding for the Kepler mission is provided by the NASA Science Mission Directorate. STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5–26555.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Jara-Maldonado, M., Alarcon-Aquino, V., Rosas-Romero, R. (2020). A Multiresolution Machine Learning Technique to Identify Exoplanets. In: MartÃnez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Soft Computing. MICAI 2020. Lecture Notes in Computer Science(), vol 12468. Springer, Cham. https://doi.org/10.1007/978-3-030-60884-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-60884-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60883-5
Online ISBN: 978-3-030-60884-2
eBook Packages: Computer ScienceComputer Science (R0)