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Open Astronomy

formerly Baltic Astronomy

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IMPACT FACTOR 2017 (Baltic Astronomy): 0.417
5-year IMPACT FACTOR (Baltic Astronomy): 0.486

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Volume 26, Issue 1


The applications of deep neural networks to sdBV classification

Thomas M. Boudreaux
Published Online: 2017-12-29 | DOI: https://doi.org/10.1515/astro-D-17-0450


With several new large-scale surveys on the horizon, including LSST, TESS, ZTF, and Evryscope, faster and more accurate analysis methods will be required to adequately process the enormous amount of data produced. Deep learning, used in industry for years now, allows for advanced feature detection in minimally prepared datasets at very high speeds; however, despite the advantages of this method, its application to astrophysics has not yet been extensively explored. This dearth may be due to a lack of training data available to researchers. Here we generate synthetic data loosely mimicking the properties of acoustic mode pulsating stars and we show that two separate paradigms of deep learning - the Artificial Neural Network And the Convolutional Neural Network - can both be used to classify this synthetic data effectively. And that additionally this classification can be performed at relatively high levels of accuracy with minimal time spent adjusting network hyperparameters.

Keywords : Deep Learning; sdBV


  • LSST Science Collaboration, Abell, P. A., Allison, J., Anderson, S. F., Andrew, J. R., Angel, J. R. P., Armus, L. D. et al. 2009, LSST Science Book, Version 2.0, ArXiv: 0912.0201. Google Scholar

  • Molnar, L. A., Van Noord, D. , Kinemuchi, K. , Smolinski, J. P., Alexander, C. E., Kobulnicky, H. A. et al. 2017, KIC 9832227: a red nova precursor. In American Astronomical Society Meeting Abstracts, 229.Google Scholar

  • George, D. and Huerta, E. A. 2017, ArXiv:1711.03121.Google Scholar

  • Huertas-Company, M., Gravet, R., Cabrera-Vives, G., Pérez-González, P. G., Kartaltepe, J. S. , Barro, G. , et al. 2015, ApJL, 221, 8, 10.1088/0067-0049/221/1/8.Google Scholar

  • Hon, M., Stello, D., and Yu, J. 2017, MNRAS, 469, 4578-4583, 10.1093/mnras/stx1174.Heber, U. 2016, PASP, 128, 082001, 10.1088/1538-3873/128/966/082001.Google Scholar

  • Schmidhuber, J. 2016, Neural Networks, 61, 85 - 117, https://doi.org/10.1016/j.neunet.2014.09.003.CrossrefGoogle Scholar

  • Chollet, F. et al. 2015, Keras, https://github.com/fchollet/keras. Google Scholar

  • Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C. et al. 2015, Software: TensorFlow, https://www.tensorflow.org/.Google Scholar

  • Boudreaux, T. 2017, Tboudreaux/astrosynth version 0.6 - beta, http://doi.org/10.5281/zenodo.998635.Google Scholar

  • Rosenblatt, F. 1958, Psychological review, 65(6), 386-408. CrossrefGoogle Scholar

  • van der Walt, S., Colbert, S. C., and Varoquaux, G. 2011, Computing in Science & Engineering, 13(2), 22-30.Google Scholar

  • Jones, E., Oliphant, T., Pearu Peterson, et al. 2001, SciPy: Open source scientific tools for Python, http://www.scipy.org/.Google Scholar

  • Boudreaux, T. M., Barlow, B. N., FlemingS. W., Vasquez Soto, A., Million, C., Reichart, D. E. et al. 2017, ApJ, 845, 171, 10.3847/1538-4357/aa8263.Google Scholar

About the article

Received: 2017-09-29

Accepted: 2017-11-30

Published Online: 2017-12-29

Published in Print: 2017-12-20

Citation Information: Open Astronomy, Volume 26, Issue 1, Pages 258–269, ISSN (Online) 2543-6376, DOI: https://doi.org/10.1515/astro-D-17-0450.

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© 2018. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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