Abstract
We developed an automated method for sunspot detection using digital white-light solar images to achieve a performance similar to that of visual drawing observations in sunspot counting. To identify down to small, isolated spots correctly, we pay special attention to the accurate derivation of the quiet-disk component of the Sun, which is used as a reference to identify sunspots using a threshold. This threshold is determined using an adaptive method to process images obtained under various conditions. To eliminate the seeing effect, our method can process multiple images taken within a short time. We applied the developed method to digital images captured at three sites and compared the detection results with those of visual observations. We conclude that the proposed sunspot detection method has a similar performance to that of visual observation. This method can be widely used by public observatories and amateurs as well as professional observatories as an alternative to hand-drawn visual observation for sunspot counting.
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References
Aschwanden, M.J.: 2010, Image processing techniques and feature recognition in solar physics. Solar Phys. 262, 235. DOI. ADS.
Beck, J.G., Chapman, G.A.: 1993, A study of the contrast of sunspots from photometric images. Solar Phys. 146, 49. DOI. ADS.
Carvalho, S., Gomes, S., Barata, T., Lourenço, A., Peixinho, N.: 2020, Comparison of automatic methods to detect sunspots in the Coimbra observatory spectroheliograms. Astron. Comput. 32, 100385. DOI. ADS.
Chapman, G.A.: 1994, Photometric observations of the sun. In: Pap, J.M., Frohlich, C., Hudson, H.S., Solanki, S.K. (eds.) Invited Papers from IAU Colloquium 143: The Sun as a Variable Star: Solar and Stellar Irradiance Variations, 117. ADS.
Clette, F., Svalgaard, L., Vaquero, J.M., Cliver, E.W.: 2014, Revisiting the sunspot number. A 400-year perspective on the Solar cycle. Space Sci. Rev. 186, 35. DOI. ADS.
Colak, T., Qahwaji, R.: 2008, Automated McIntosh-based classification of sunspot groups using MDI images. Solar Phys. 248, 277. DOI. ADS.
Cortesi, S., Cagnotti, M., Bianda, M., Ramelli, R., Manna, A.: 2016, Sunspot observations and counting at Specola Solare Ticinese in Locarno since 1957. Solar Phys. 291, 3075. DOI. ADS.
Curto, J.J., Blanca, M., Martínez, E.: 2008, Automatic sunspots detection on full-disk solar images using mathematical morphology. Solar Phys. 250, 411. DOI. ADS.
Goel, S., Mathew, S.K.: 2014, Automated detection, characterization, and tracking of sunspots from SoHO/MDI continuum images. Solar Phys. 289, 1413. DOI. ADS.
Hanaoka, Y., Sakurai, T., Otsuji, K., Suzuki, I., Morita, S.: 2020, Synoptic solar observations of the Solar Flare Telescope focusing on space weather. J. Space Weather Space Clim. 10, 41. DOI. ADS.
Imai, H., Suematsu, Y., Miyashita, M., Kumagai, K.: 1998, Development of automatic detection and reduction system for sunspots and faculae. Rep. Nat. Astron. Observ. Japan 4, 1. ADS.
Pierce, A.K., Slaughter, C.D.: 1977, Solar limb darkening. I: lambda lambda (3033 – 7297). Solar Phys. 51, 25. DOI. ADS.
Preminger, D.G., Walton, S.R., Chapman, G.A.: 2001, Solar feature identification using contrasts and contiguity. Solar Phys. 202, 53. DOI. ADS.
Sakurai, T., Suematsu, Y.: 2002, Observational studies of the solar cycle at the National Astronomical Observatory of Japan. Adv. Space Res. 29, 1565. DOI. ADS.
Sakurai, T., Ichimoto, K., Nishino, Y., Shinoda, K., Noguchi, M., Hiei, E., Li, T., He, F., Mao, W., Lu, H., Ai, G., Zhao, Z., Kawakami, S., Chae, J.-C.: 1995, Solar Flare Telescope at Mitaka. Publ. Astron. Soc. Japan 47, 81. ADS.
Sakurai, T., Hanaoka, Y., Arai, T., Hagino, M., Kawate, T., Kitagawa, N., Kobiki, T., Miyashita, M., Morita, S., Otsuji, K., Shinoda, K., Suzuki, I., Yaji, K., Yamasaki, T., Fukuda, T., Noguchi, M., Takeyama, N., Kanai, Y., Yamamuro, T.: 2018, Infrared spectro-polarimeter on the Solar Flare Telescope at NAOJ/Mitaka. Publ. Astron. Soc. Japan 70, 58. DOI. ADS.
Scherrer, P.H., Bogart, R.S., Bush, R.I., Hoeksema, J.T., Kosovichev, A.G., Schou, J., Rosenberg, W., Springer, L., Tarbell, T.D., Title, A., Wolfson, C.J., Zayer, I. (MDI Engineering Team): 1995, The solar oscillations investigation – Michelson Doppler imager. Solar Phys. 162, 129. DOI. ADS.
Scherrer, P.H., Schou, J., Bush, R.I., Kosovichev, A.G., Bogart, R.S., Hoeksema, J.T., Liu, Y., Duvall, T.L., Zhao, J., Title, A.M., Schrijver, C.J., Tarbell, T.D., Tomczyk, S.: 2012, The Helioseismic and Magnetic Imager (HMI) Investigation for the Solar Dynamics Observatory (SDO). Solar Phys. 275, 207. DOI. ADS.
Steinegger, M., Brandt, P.N., Haupt, H.F.: 1996, Sunspot irradiance deficit, facular excess, and the energy balance of solar active regions. Astron. Astrophys. 310, 635. ADS.
Steinegger, M., Brandt, P.N., Pap, J., Schmidt, W.: 1990, Sunspot photometry and the total solar irradiance deficit measured in 1980 BY ACRIM. Astrophys. Space Sci. 170, 127. DOI. ADS.
Turmon, M., Pap, J.M., Mukhtar, S.: 2002, Statistical pattern recognition for labeling solar active regions: application to SOHO/MDI imagery. Astrophys. J. 568, 396. DOI. ADS.
Watson, F., Fletcher, L., Dalla, S., Marshall, S.: 2009, Modelling the longitudinal asymmetry in sunspot emergence: the role of the Wilson depression. Solar Phys. 260, 5. DOI. ADS.
Yang, Y., Yang, H., Bai, X., Zhou, H., Feng, S., Liang, B.: 2018, Automatic detection of sunspots on full-disk solar images using the simulated annealing genetic method. Publ. Astron. Soc. Pac. 130, 104503. DOI. ADS.
Zhao, C., Lin, G., Deng, Y., Yang, X.: 2016, Automatic recognition of sunspots in HSOS full-disk solar images. Publ. Astron. Soc. Aust. 33, e018. DOI. ADS.
Zharkov, S., Zharkova, V.V., Ipson, S.S.: 2005, Statistical properties of sunspots in 1996 2004: I. Detection, North South asymmetry and area distribution. Solar Phys. 228, 377. DOI. ADS.
Zharkova, V.V., Ipson, S.S., Zharkov, S.I., Benkhalil, A., Aboudarham, J., Bentley, R.D.: 2003, A full-disk image standardisation of the synoptic solar observations at the Meudon Observatory. Solar Phys. 214, 89. DOI. ADS.
Acknowledgments
We thank the Kawaguchi Science Museum and Mr. S. Morita, who provided digital white-light images of the Sun. The Specola Solare Ticinese and Kwasan Observatory kindly permitted us to use their visual sunspot observation data for our analysis. HMI data used in this study were courtesy of NASA/SDO and the HMI science team. SDO is a mission for NASA’s Living With a Star program. The source of the international sunspot number is WDC-SILSO, Royal Observatory of Belgium, Brussels.
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Y. H. developed a software for the automated sunspot detection described in the paper, and evaluated its performance. He wrote the manuscript as well.
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Hanaoka, Y. Automated Sunspot Detection as an Alternative to Visual Observations. Sol Phys 297, 158 (2022). https://doi.org/10.1007/s11207-022-02089-z
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DOI: https://doi.org/10.1007/s11207-022-02089-z