Solar flares, sudden bursts of intense electromagnetic radiation from the Sun, can significantly disrupt technological infrastructure, including communication and navigation satellites. To mitigate these risks, accurate forecasting of solar activity is crucial. This study investigates the potential of the Sun’s background X-ray flux as a tool for predicting solar flares. We analyzed data collected by solar telescopes and satellites between the years 2013 and 2023, focusing on the duration, frequency, and intensity of solar flares. We compared these characteristics with the background X-ray flux at the time of each flare event. Our analysis employed statistical methods to identify potential correlations between these solar phenomena. The key finding of this study reveals a significant positive correlation between solar flare activity and the Sun’s background X-ray flux. This suggests that these phenomena are interconnected within the framework of overall solar activity. We observed a clear trend: periods with increased occurrences of solar flares coincided with elevated background flux levels. This finding has the potential to improve solar activity forecasting. By monitoring background flux variations, we may be able to develop a more effective early warning system for potentially disruptive solar flares. This research contributes to a deeper understanding of the complex relationship between solar flares and the Sun’s overall radiative output. These findings indicate that lower-resolution X-ray sensors can be a valuable tool for identifying periods of increased solar activity by allowing us to monitor background flux variations. A more affordable approach to solar activity monitoring is advised.
References
[1]
Solanki, S.K., Usoskin, I.G., Kromer, B., Schüssler, M. and Beer, J. (2004) Unusual Activity of the Sun during Recent Decades Compared to the Previous 11, 000 Years. Nature, 431, 1084-1087. https://doi.org/10.1038/nature02995
[2]
Zhu, C., Qiu, J. and Longcope, D.W. (2018) Two-Phase Heating in Flaring Loops. The Astrophysical Journal, 856, Article 27. https://doi.org/10.3847/1538-4357/aaad10
[3]
Kumar, A. and Kumar, S. (2014) Space Weather Effects on the Low Latitude D-Region Ionosphere during Solar Minimum. Earth, Planets and Space, 66, Article No. 76. https://doi.org/10.1186/1880-5981-66-76
[4]
Loto’aniu, P., Rodriguez, J., Redmon, R., Machol, J., Kress, B., Seaton, D., Darnel, J., Rowland, W., Tilton, M., Denig, W., Boudouridis, A., Codrescu, S. and Claycomb, A. (2017) Space Weather Monitoring with GOES-16: Instruments and Data Products. Geophysical Research Abstracts, 19, 9663.
[5]
Sammis, I., Tang, F. and Zirin, H. (2000) The Dependence of Large Flare Occurrence on the Magnetic Structure of Sunspots. The Astrophysical Journal, 540, 583-587. https://doi.org/10.1086/309303
[6]
Winter, L.M. and Balasubramaniam, K.S. (2014) Estimate of Solar Maximum Using the 1-8 Å Geostationary Operational Environmental Satellites X-Ray Measurements. The Astrophysical Journal, 793, L45. https://doi.org/10.1088/2041-8205/793/2/l45
[7]
McIntosh, P.S. (1990) The Classification of Sunspot Groups. Solar Physics, 125, 251-267. https://doi.org/10.1007/bf00158405
[8]
Lee, K., Moon, Y.J., Lee, J., Lee, K. and Na, H. (2012) Solar Flare Occurrence Rate and Probability in Terms of the Sunspot Classification Supplemented with Sunspot Area and Its Changes. Solar Physics, 281, 639-650. https://doi.org/10.1007/s11207-012-0091-9
[9]
Winter, L.M. and Balasubramaniam, K. (2015) Using the Maximum X-Ray Flux Ratio and X-Ray Background to Predict Solar Flare Class. Space Weather, 13, 286-297. https://doi.org/10.1002/2015sw001170
[10]
Tlatov, A.G., Illarionov, E.A., Berezin, I.A. and Shramko, A.D. (2020) Prediction of Solar Flares and Background Fluxes of X-Ray Radiation According to Synoptic Ground-Based Observations Using Machine-Learning Models. Cosmic Research, 58, 444-449. https://doi.org/10.1134/s0010952520060106
[11]
Qahwaji, R. and Colak, T. (2007) Automatic Short-Term Solar Flare Prediction Using Machine Learning and Sunspot Associations. Solar Physics, 241, 195-211. https://doi.org/10.1007/s11207-006-0272-5
[12]
Yi, K., Moon, Y., Shin, G. and Lim, D. (2020) Forecast of Major Solar X-Ray Flare Flux Profiles Using Novel Deep Learning Models. The Astrophysical Journal Letters, 890, L5. https://doi.org/10.3847/2041-8213/ab701b