Inference of magnetic field during the Dalton minimum: Case study with recorded sunspot areas. (23rd June 2022)

• Record Type: Journal Article
• Authors: Uneme, Shoma; Imada, Shinsuke; Lee, Harim; Park, Eunsu; Hayakawa, Hisashi; Iju, Tomoya; Moon, Yong-Jae
• Is Part Of: Publications of the Astronomical Society of Japan. Volume 74:Number 4(2022)
• Page Start: 767
• Page End: 776
• Publication Date: 2022-06-23
• Subjects: Sun: Dalton minimum -- Sun: machine learning -- Sun: sunspot; Astronomy -- Periodicals; 520.5
• DOI: 10.1093/pasj/psac032
• Languages: English
• ISSNs: 0004-6264

Abstract

Abstract: The Dalton minimum is considered to be one of the unique solar activity periods that have been captured in direct sunspot observations since 1610. Specifically, the solar magnetic field in this period is of great interest. Derfflinger and Prantner's sunspot observations of 1802–1824 and 1800–1844 are the most important references for this period. To understand the solar magnetic activity in the Dalton minimum, it is important to estimate the latitude/longitude distribution of the sunspots and the sunspot areas for that duration. In this study, we analyze Derfflinger and Prantner's sunspot drawings to determine the sunspot parameters, particularly the sunspot area. We find that the sunspot areas obtained from Derfflinger's drawings are overemphasized by a factor of eight relative to those derived from modern observations. We also analyze Prantner's sunspot drawings to validate our analysis of Derfflinger's drawings. Further, we generate solar magnetograms from Derfflinger's sunspot drawings using a deep-learning model based on conditional generative adversarial networks. Our analysis of these sunspot areas will provide important information for restoring the magnetograms during the Dalton minimum.