Predicting sunspot variations through neural network

doi:10.7523/j.ucas.2021.0068

Abstract

Abstract: Sunspot variations are the sun's symptoms of strong magnetic perturbations. In this paper, we use long short-term memory neural network and one-dimensional convolution neural network to detect sunspot variations. Here we use three different datasets, including the yearly mean sunspot number (YSSN) from 1700 to 2020, the monthly mean sunspot number (MSSN) from 1749 to 2021 and the monthly mean sunspot areas (MSSA) from 1874 to 2021. First, based on the YSSN dataset, we obtain YSSN for 2021 and the predicted YSSN in the 25th solar cycle appears at 2025 which equals 163.4; Then, based on the MSSN dataset, we obtain MSSN for June 2021 and the predicted YSSN in the 25th solar cycle appears in October 2024 which equals 245.9; Next, based on the MSSA dataset, the predicted MSSA for June 2021 is 73.1; Finally, the latitude is divided into 13 partitions to predict the butterfly diagram, and still, neural network can reconstruct the butterfly diagram. Therefore, neural network can provide a physical perspective for sunspot investigation.

Key words: number of sunspots, area of sunspots, solar cycle, butterfly diagram, neural network

CLC Number:

P182.9

CHENG Shu, SHI Yaolin, ZHANG Huai. Predicting sunspot variations through neural network[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(5): 615-626.

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