Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
권호 표지
DOI QR코드

DOI QR Code

Development of a Python-based Algorithm for Image Analysis of Outer-ring Galaxies

외부고리 은하 영상 분석을 위한 파이썬 기반 알고리즘 개발

  • Jo, Hoon (Department of Earth Science Education, Korea National University of Education) ;
  • Sohn, Jungjoo (Department of Earth Science Education, Korea National University of Education)
  • 조훈 (한국교원대학교 지구과학교육과) ;
  • 손정주 (한국교원대학교 지구과학교육과)
  • Received : 2022.10.14
  • Accepted : 2022.10.31
  • Published : 2022.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, we aimed to develop a Python-based outer-ring galaxy analysis algorithm according to the data science process. We assumed that the potential users are citizen scientists, including students and teachers. In the actual classification studies using real data of galaxies, a specialized software called IRAF is used, thereby limiting the general public's access to the software. Therefore, an image analysis algorithm was developed for the outer-ring galaxies as targets, which were compared with those of the previous research. The results of this study were compared with those of studies conducted using IRAF to verify the performance of the newly developed image analysis algorithm. Among the 69 outer-ring galaxies in the first test, 50 cases (72.5%) showed high agreement with the previous research. The remaining 19 cases (27.5%) showed differences that were caused by the presence of bright stars overlapped in the line of sight or weak brightness in the inner galaxy. To increase the usability of the finished product that has undergone a supplementary process, all used data, algorithms, Python code files, and user manuals were loaded in GitHub and made available as shared educational materials.

본 연구는 데이터 과학의 과정에 따른 파이썬 기반의 외부고리 은하 영상 분석 알고리즘 개발을 목적으로 한다. 잠재적 사용자는 학생과 교사를 포함한 시민 과학자로 정하였다. 은하의 실제 데이터를 이용한 분류 연구는 IRAF 라는 전문 소프트웨어가 이용되고 있어 일반인이 접근하기에 한계가 있다. 이에 IRAF를 사용한 선행 연구의 결과와 비교 검증이 가능한 외부고리 은하를 분석 대상 천체로 정하여, 영상 분석 알고리즘을 개발하고 그 결과를 검증하였다. 검증 결과 총 69개의 외부고리 은하 중 50개(72.5%)가 IRAF 결과와 높은 일치를 보였다. 남은 19개(27.5%)는 시선 방향에 겹친 밝은 별의 존재 혹은 은하 내부의 약한 밝기로 인해 IRAF 결과와 다른 낮은 일치를 보였다. 보완 과정을 거친 최종 결과물은 공유 및 교육 자료의 활용도를 높이기 위해 전체 사용된 데이터와 알고리즘, 파이썬 코드 파일 및 사용 설명서를 GitHub에 탑재하였다.

Keywords

References

  1. Ali, J., Khan, R., Ahmad, N., and Maqsood, I., 2012, Random forests and decision trees. International Journal of Computer Science Issues, 9(5), 272-278.
  2. Binney, J., Michael, M., and Merrifield, M., 1998, Galactic astronomy. Princeton University Press, New Jersey, USA, 816 p.
  3. Bogdanchikov, A., Zhaparov, M., and Suliyev, R., 2013, Python to learn programming. Paper presented at the Journal of Physics: Conference Series, 423(1), 012027. https://doi.org/10.1088/1742-6596/423/1/012027
  4. Bradley, L., Sipocz, B., Robitaille, T., Tollerud, E., Deil, C., Vinicius, Z., Barbary, K., Gunther, H.M., Bostroem, A., Droettboom, M., Bray, E., Bratholm, L.A., ..., and Weaver, B.A., 2016, Photutils: Photometry tools. Astrophysics Source Code Library ascl-1609.
  5. Buta, R., 1995, The catalog of southern ringed galaxies. The Astrophysical Journal Supplement Series, 96, 39-116. https://doi.org/10.1086/192113
  6. Chang, H.H., Sohn, J., and Ahn, H.B., 2020, A Study of Outer ring Galaxies within z<0.05. Journal of the Korean Earth Science Society, 41(3), 211-221. (in Korean) https://doi.org/10.5467/JKESS.2020.41.3.211
  7. de Vaucouleurs, G.H., 1963, Revised Classification of 1500 Bright Galaxies. The Astrophysical Journal Supplement Series, 8, 31-97. https://doi.org/10.1086/190084
  8. Dehnen, W., 2000, The effect of the outer lindblad resonance of the galactic bar on the local stellar velocity distribution. The Astronomical Journal, 119(2), 800-812. https://doi.org/10.1086/301226
  9. Elmegreen, D.M., Elmegreen, B.G., Combes, F., and Bellin, A.D., 1992, The influence of environment on outer rings and pseudo-rings in galaxies. Astronomy and Astrophysics, 257, 17-23.
  10. Gomez, E.L. and Fitzgerald, M.T., 2017, Robotic telescopes in education. Astronomical Review, 13(1), 28-68. https://doi.org/10.1080/21672857.2017.1303264
  11. Guo, H., 2017, Big earth data: A new frontier in earth and information sciences. Big Earth Data, 1(1-2), 4-20. https://doi.org/10.1080/20964471.2017.1403062
  12. Hubble, E.P., 1926, Extragalactic nebulae. Astrophysical Journal, 64, 321-369. https://doi.org/10.1086/143018
  13. Image Reduction and Analysis Facility [IRAF], 2022, IRAF community. https://iraf.net/ (October 26th, 2022)
  14. Jeon, I.Y., 2022, Development of 'Hubble-Lemaitre Law' education program based on scientific History using Python. Unpublished M.S. dissertation, Korea National University of Education, Chungbuk, Korea, 99 p. (in Korean)
  15. Kim, M.L. and Sohn, J., 2022, Development of 'H-R Diagram' Teacher Education Program Applying Data Science. School Science Journal, 16(1), 59-74. (in Korean) https://doi.org/10.15737/SSJ.16.1.202202.59
  16. Kwon, Y.J., Jeong, J.S., Shinm D.H., Lee, J,K., Lee, I.S., and Byeon, J.H., 2011, Generation and evaluation of scientific knowledge to improve scientific inquiry and thinking skills. Hakjisa, Seoul, Korea, 384 p. (in Korean)
  17. Lee, K.Y., Kim, H.S., Park, J.Y., Lee, S.M., Jeong, J.H., and Choi, Y.O., 2018, Earth Science I. Visang, Seoul, Korea, 240 p. (in Korean)
  18. Ministry of Education [MOE], 2015a, 2015 revised science curriculum. No. 2015-74(1). Ministry of Education, Sejong, Korea (in Korean), 48 p.
  19. Ministry of Education [MOE], 2015b, 2015 revised science curriculum. No. 2015-74(9). Ministry of Education, Sejong, Korea (in Korean), 278 p.
  20. Robitaille, T.P., Tollerud, E.J., Greenfield, P., Droettboom, M., Bray, E., Aldcroft, T., Davis, M., Ginsburg, A., Price-Whelan, A.M., Kerzendorf, W.E., Conley, A., Crighton, N., ..., and Streicher, O., 2013, Astropy: A community Python package for astronomy. Astronomy & Astrophysics, 558, A33. https://doi.org/10.1051/0004-6361/201322068
  21. Sandage, A., 1961, The Hubble atlas of galaxies. Carnegie Institution of Washington, Washington D.C., USA, 150p.
  22. Schutt, R. and O'Neil, C., 2013, Doing data science: Straight talk from the frontline. O'Reilly Media, Sebastopol, California, USA, 619 p.
  23. Schwarz, M.P., 1981, The response of gas in a galactic disk to bar forcing. The Astrophysical Journal, 247, 77-88. https://doi.org/10.1086/159011
  24. Sloan Digital Sky Survey [SDSS], 2022, Image navigator(archive) of SDSS DR7 http://cas.sdss.org/dr7/en/tools/chart/navi.asp (November 2nd, 2022)
  25. Styers, D.M., 2018, Using big data to engage undergraduate students in authentic science. Journal of Geoscience Education, 66(1), 12-24. https://doi.org/10.1080/10899995.2018.1411699
  26. Wilman, D.J. and Erwin, P., 2012, The Relation between Galaxy Morphology and Environment in the Local Universe: An RC3-SDSS Picture. The Astrophysical Journal, 746(2), 1-22. https://doi.org/10.1088/0004-637X/746/1/1