Increasing Lifetime of Recurrent Sunspot Groups Within the Greenwich Photoheliographic Results
- 100 Downloads
Long-lived (>20 days) sunspot groups extracted from the Greenwich Photoheliographic Results (GPR) are examined for evidence of decadal change. The problem of identifying sunspot groups that are observed on consecutive solar rotations (recurrent sunspot groups) is tackled by first constructing manually an example dataset of recurrent sunspot groups and then using machine learning to generalise this subset to the whole GPR. The resulting dataset of recurrent sunspot groups is verified against previous work by A. Maunder and other Royal Greenwich Observatory (RGO) compilers. Recurrent groups are found to exhibit a slightly larger value for the Gnevyshev – Waldmeier Relationship than the value found by Petrovay and van Driel-Gesztelyi (Solar Phys. 51, 25, 1977), who used recurrence data from the Debrecen Photoheliographic Results. Evidence for sunspot-group lifetime change over the previous century is observed within recurrent groups. A lifetime increase of a factor of 1.4 between 1915 and 1940 is found, which closely agrees with results from Blanter et al. (Solar Phys. 237, 329, 2006). Furthermore, this increase is found to exist over a longer period (1915 to 1950) than previously thought and provisional evidence is found for a decline between 1950 and 1965. Possible applications of machine-learning procedures to the analysis of historical sunspot observations, the determination of the magnetic topology of the solar corona and the incidence of severe space–weather events are outlined briefly.
KeywordsSunspots Neural networks Long-term change Non-linear Lifetime Greenwich Sunspot nests Sunspot nestlet
Unable to display preview. Download preview PDF.
- Dezső, L., Gerlei, O., Kovács, Á.: 1987, Debrecen Photoheliographic Results for the Year 1977, Heliogr. Series 1, Publ. Debrecen Obs., Debrecen. Google Scholar
- Fawcett, T.: 2004, Technical Report, HP Laboratories, Palo Alto. Google Scholar
- Haigh, J.D., Lockwood, M., Giampapa, M.S., Rüedi, I., Güdel, M., Schmutz, W.: 2005, The Sun, Solar Analogs and the Climate, Springer, Berlin. Google Scholar
- Henwood, R.: 2008, Master’s Thesis, Centre for Fusion, Space and Astrophysics, Univ. Warwick. Google Scholar
- Kohavi, R.: 1995, In: Proceedings of the Fourteenth International Joint Conferences on Artificial Intelligence, 2, Morgan Kaufmann, Montreal, 1137. Google Scholar
- Meeus, J.H.: 1991, Astronomical Algorithms, Willmann-Bell, Richmond. Google Scholar
- Moore, A.W.: 2001, Cross-validation for detecting and preventing overfitting. http://www.cs.cmu.edu/afs/cs/user/awm/web/tutorials/overfit10.pdf.
- Provost, F.: 2000, Machine learning from imbalanced data sets 101 (Extended Abstract), Association for the Advancement of Artificial Intelligence Workshop on Imbalanced Data Sets, Austin, Texas. Google Scholar
- Royal Greenwich Observatory: 1980, Royal Observatory Annals, Photoheliographic Results 1972 – 1976, Royal Greenwich Observatory, Herstmonceux. Google Scholar
- Thompson, M.J., Toomre, J., Anderson, E.R., Antia, H.M., Berthomieu, G., Burtonclay, D., Chitre, S.M., Christensen-Dalsgaard, J., Corbard, T., Derosa, M., Genovese, C.R., Gough, D.O., Haber, D.A., Harvey, J.W., Hill, F., Howe, R., Korzennik, S.G., Kosovichev, A.G., Leibacher, J.W., Pijpers, F.P., Provost, J., Rhodes, E.J. Jr., Schou, J., Sekii, T., Stark, P.B., Wilson, P.R.: 1996, Science 272(5266), 1300. CrossRefADSGoogle Scholar
- Waldmeier, M.: 1955, Ergebnisse und Probleme der Sonnenforschung, Geest and Portig, Leipzig. Google Scholar