Classification of Eclipsing Binary Light Curves with Deep Learning Neural Network Algorithms

Abstract

We present an image classification algorithm utilising a deep learning convolutional neural network architecture, which categorises the morphologies of eclipsing binary systems based on their light curves. The algorithm trains the machine with light curve images generated from the observational data of eclipsing binary stars in contact, detached and semi-detached morphologies, whose light curves are provided by Kepler, ASAS and CALEB catalogues. The structure of the architecture is explained, the parameters of the network layers and the resulting metrics are discussed. Our results show that the algorithm, which is selected among 132 neural network architectures, estimates the morphological classes of an independent validation dataset, 705 true data, with an accuracy of 92%.

Keywords

• (stars:) binaries: eclipsing
• methods: data analysis
• techniques: image processing

Derin Öğrenme Sinir Ağı Algoritmaları ile İkili Işık Eğrilerinin Sınıflandırılması

Öz

Bu çalışmada, gözlemsel ışık eğrilerini kullanarak değen, ayrık ve yarı-ayrık yapıya sahip çift yıldız sistemlerini sınıflandıran bir görüntü sınıflandırma algoritması sunulmuştur. Algoritma, Kepler, ASAS ve CALEB kataloglarından alınan çift yıldız sistemlerine ait ışık eğrilerinden üretilen görüntülerle eğitilmiş bir derin öğrenme evrişimsel sinir ağı (Convolutional Neural Network, CNN) mimarisi temellidir. Modelin mimari yapısı açıklanmış, ağ katmanlarının parametreleri ve elde edilen performans metrikleri tartışılmıştır. Sonuçlar, 132 farklı sinir ağı mimarisi arasından seçilen algoritmanın, 705 gözlemsel veriden oluşan bağımsız bir doğrulama veri kümesi üzerinde yapısal sınıflandırmayı %92 doğrulukla tahmin edebildiğini göstermektedir.

Anahtar Kelimeler

• (stars:) binaries: eclipsing
• methods: data analysis
• techniques: image processing

References

1. Abadi M., et al., 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow.org/
2. Basha S. S., Dubey S. R., Pulabaigari V., Mukherjee S., 2020, Neurocomputing (doi:https://doi.org/10.1016/j.neucom.2019.10.008), 378, 112
3. Birky J., Davenport J., Brandt T., 2020, in American Astronomical Society Meeting Abstracts \#235. p. 170.20
4. Bódi A., Hajdu T., 2021, ApJS (doi:10.3847/1538-4365/ac082c), 255, 1
5. Bradstreet D. H., 2005, Society for Astronomical Sciences Annual Symposium, 24, 23, ADS:https://ui.adsabs.harvard.edu/abs/2005SASS...24...23B
6. Bradstreet D. H., Steelman D. P., Sanders S. J., Hargis J. R., 2004, in American Astronomical Society Meeting Abstracts \#204. p. 05.01
7. Chollet F., et al., 2015, Keras, https://keras.io
8. Christlein V., Spranger L., Seuret M., Nicolaou A., Král P., Maier A., 2019, in 2019 International Conference on Document Analysis and Recognition (ICDAR). pp 1090--1096, doi:10.1109/ICDAR.2019.00177

9. Cokina M., Maslej-Krešň'akov'a V., Butka P., Parimucha S., 2021a, Astronomy and Computing (doi:https://doi.org/10.1016/j.ascom.2021.100488), p. 100488
10. Cokina M., Fedurco M., Parimucha S., 2021b, A\&A (doi:10.1051/0004-6361/202039171), 652, A156
11. Cortes C., Mohri M., Rostamizadeh A., 2009, in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. UAI '09. AUAI Press, Arlington, Virginia, USA, p. 109–116
12. Dai Z., Liu H., Le Q. V., Tan M., 2021, preprint, , ADS:https://ui.adsabs.harvard.edu/abs/2021arXiv210604803D
13. Fukushima K., 1980, Biological Cybernetics, 36, 193
14. Gavrikov P., 2020, visualkeras, https://github.com/paulgavrikov/visualkeras
15. G\'eron A., 2017, Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 1st edn. O'Reilly Media, Inc., p. 357
16. Goodfellow I. J., Bengio Y., Courville A., 2016, Deep Learning. MIT Press, Cambridge, MA, USA, p. 171
17. Guinan E. F., 1993, in Leung K.-C., Nha I.-S., eds, Astronomical Society of the Pacific Conference Series Vol. 38, New Frontiers in Binary Star Research. p. 1
18. Harris D., Harris S., 2007, Digital Design and Computer Architecture. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, p. 82
19. Harris C. R., et al., 2020, Nature (doi:10.1038/s41586-020-2649-2), 585, 357
20. Healy B. F., et al., 2024, ApJS (doi:10.3847/1538-4365/ad33c6), 272, 14
21. Hunter J. D., 2007, Computing in Science \& Engineering (doi:10.1109/MCSE.2007.55), 9, 90
22. Jenkins J. M., et al., 2010, ApJ (doi:10.1088/2041-8205/713/2/L87), 713, L87
23. Juran J. M., Godfrey A. B., 1999, Juran's quality handbook. 5e, McGraw Hill, http://books.google.de/books?id=beVTAAAAMAAJ
24. Kingma D. P., Ba J., 2015, in Bengio Y., LeCun Y., eds, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. p. 1, http://arxiv.org/abs/1412.6980
25. Kirk B., et al., 2016, The Astronomical Journal (doi:10.3847/0004-6256/151/3/68), 151, 68
26. Kochoska A., Conroy K., Hambleton K., Prša A., 2020, Contributions of the Astronomical Observatory Skalnat\'e Pleso (doi:10.31577/caosp.2020.50.2.539), 50
27. Kopal Z., 1955, Annales d'Astrophysique, 18, 379, ADS:https://ui.adsabs.harvard.edu/abs/1955AnAp...18..379K
28. Krizhevsky A., Sutskever I., Hinton G. E., 2012, in Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1. NIPS'12. Curran Associates Inc., Red Hook, NY, USA, p. 1097–1105
29. Lecun Y., Bottou L., Bengio Y., Haffner P., 1998, Proceedings of the IEEE (doi:10.1109/5.726791), 86, 2278
30. Matijevič G., Prša A., Orosz J. A., Welsh W. F., Bloemen S., Barclay T., 2012, The Astronomical Journal (doi:10.1088/0004-6256/143/5/123), 143, 123
31. Moore H., 1897, The ANNALS of the American Academy of Political and Social Science (doi:10.1177/000271629700900314), 9, 128
32. Murphy K. P., 2012, Machine Learning. MIT Press, London, England, p. 247
33. Parimucha S., Gajdoš P., Markus Y., Kudak V., 2024, Contributions of the Astronomical Observatory Skalnate Pleso (doi:10.31577/caosp.2024.54.2.167), 54, 167
34. Pedregosa F., et al., 2011, Journal of Machine Learning Research, 12, 2825
35. Pineau J., Vincent-Lamarre P., Sinha K., Larivière V., Beygelzimer A., d'Alché-Buc F., Fox E., Larochelle H., 2020, preprint, (arXiv:2003.12206), ADS:https://ui.adsabs.harvard.edu/abs/2020arXiv200312206P
36. Pojmanski G., 1997, Acta Astronomica, 47, 467, ADS:https://ui.adsabs.harvard.edu/abs/1997AcA....47..467P
37. Pojmanski G., 2002, AcA, 52, 397, ADS:https://ui.adsabs.harvard.edu/abs/2002AcA....52..397P
38. Popper D. M., Etzel P. B., 1981, AJ (doi:10.1086/112862), 86, 102
39. Prsa A., Wrona M., 2024, in American Astronomical Society Meeting Abstracts. p. 423.07
40. Prša A., Guinan E. F., Devinney E. J., DeGeorge M., Bradstreet D. H., Giammarco J. M., Alcock C. R., Engle S. G., 2008, The Astrophysical Journal (doi:10.1086/591783), 687, 542
41. Prša A., Zwitter T., 2005, ApJ (doi:10.1086/430591), 628, 426
42. Ricker G. R., et al., 2015, Journal of Astronomical Telescopes, Instruments, and Systems (doi:10.1117/1.JATIS.1.1.014003), 1, 014003
43. Ruder S., 2016, ArXiv, abs/1609.04747
44. Sammut C., Webb G. I., eds, 2017, Loss. Springer US, Boston, MA, p. 781, doi:10.1007/978-1-4899-7687-1-499
45. Shorten C., Khoshgoftaar T., 2019, Journal of Big Data (doi:https://doi.org/10.1186/s40537-019-0197-0), 6, 60
46. Southworth J., Smalley B., Maxted P. F. L., Claret A., Etzel P. B., 2005, MNRAS (doi:10.1111/j.1365-2966.2005.09462.x), 363, 529
47. Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R., 2014, Journal of Machine Learning Research:http://jmlr.org/papers/v15/srivastava14a.html, 15, 1929
48. Szklenár T., Bódi A., Tarczay-Nehéz D., Vida K., Mezo G., Szabó R., 2022, ApJ (doi:10.3847/1538-4357/ac8df3), 938, 37
49. The Pandas Development Team 2020, pandas-dev/pandas: Pandas, doi:10.5281/zenodo.3509134, https://doi.org/10.5281/zenodo.3509134
50. Ting K. M., 2010, in Sammut C., Webb G. I., eds, , Encyclopedia of Machine Learning. Springer US, Boston, MA, p. 781, doi:10.1007/978-0-387-30164-8_652, https://doi.org/10.1007/978-0-387-30164-8_652
51. Tsoumakas G., Vlahavas I., 2007, in Kok J. N., Koronacki J., Mantaras R. L. d., Matwin S., Mladenič D., Skowron A., eds, Machine Learning: ECML 2007. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 406--417
52. Udalski A., Szymanski M., Kubiak M., Pietrzynski G., Wozniak P., Zebrun K., 1998, Acta Astron., 48, 147, ADS:https://ui.adsabs.harvard.edu/abs/1998AcA....48..147U
53. Ulaş B., 2020, preprint, , ADS:https://ui.adsabs.harvard.edu/abs/2020arXiv201208435U
54. Van Rossum G., 2020, The Python Library Reference, release 3.8.2. Python Software Foundation
55. Van Rossum G., Drake F. L., 2009, Python 3 Reference Manual. CreateSpace, Scotts Valley, CA
56. Wes McKinney 2010, in Stéfan van der Walt Jarrod Millman eds, Proceedings of the 9th Python in Science Conference. pp 56 -- 61, doi:10.25080/Majora-92bf1922-00a
57. Wilson R. E., Devinney E. J., 1971, ApJ (doi:10.1086/150986), 166, 605
58. Wyrzykowski L., et al., 2003, Acta Astron., 53, 1, ADS:https://ui.adsabs.harvard.edu/abs/2003AcA....53....1W
59. Yao Y., Rosasco L., Caponnetto A., 2007, Constr. Approx, pp 289--315
60. Zhou Z., Huang H., Fang B., 2021, Journal of Computer and Communications (doi:10.4236/jcc.2021.911001), 09, 1