Group Sunspot Numbers: A New Reconstruction of Sunspot Activity Variations from Historical Sunspot Records Using Algorithms from Machine Learning

Solar Physics ◽  
2022 ◽  
Vol 297 (1) ◽  
Author(s):  
Víctor Manuel Velasco Herrera ◽  
Willie Soon ◽  
Douglas V. Hoyt ◽  
Judit Muraközy
2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Rainer Arlt ◽  
José M. Vaquero

AbstractSunspot observations are available in fairly good numbers since 1610, after the invention of the telescope. This review is concerned with those sunspot observations of which longer records and drawings in particular are available. Those records bear information beyond the classical sunspot numbers or group sunspot numbers. We begin with a brief summary on naked-eye sunspot observations, in particular those with drawings. They are followed by the records of drawings from 1610 to about 1900. The review is not a compilation of all known historical sunspot information. Some records contributing substantially to the sunspot number time series may therefore be absent. We also glance at the evolution of the understanding of what sunspots actually are, from 1610 to the 19th century. The final part of the review illuminates the physical quantities that can be derived from historical drawings.


Solar Physics ◽  
2003 ◽  
Vol 218 (1/2) ◽  
pp. 295-305 ◽  
Author(s):  
I.G. Usoskin ◽  
K. Mursula ◽  
G.A. Kovaltsov

2017 ◽  
Vol 145 (13) ◽  
pp. 2640-2655 ◽  
Author(s):  
S. TOWERS

SUMMARYSince 1978, a series of papers in the literature have claimed to find a significant association between sunspot activity and the timing of influenza pandemics. This paper examines these analyses, and attempts to recreate the three most recent statistical analyses by Ertel (1994), Tappinget al.(2001), and Yeung (2006), which all have purported to find a significant relationship between sunspot numbers and pandemic influenza. As will be discussed, each analysis had errors in the data. In addition, in each analysis arbitrary selections or assumptions were also made, and the authors did not assess the robustness of their analyses to changes in those arbitrary assumptions. Varying the arbitrary assumptions to other, equally valid, assumptions negates the claims of significance. Indeed, an arbitrary selection made in one of the analyses appears to have resulted in almost maximal apparent significance; changing it only slightly yields a null result. This analysis applies statistically rigorous methodology to examine the purported sunspot/pandemic link, using more statistically powerful un-binned analysis methods, rather than relying on arbitrarily binned data. The analyses are repeated using both the Wolf and Group sunspot numbers. In all cases, no statistically significant evidence of any association was found. However, while the focus in this particular analysis was on the purported relationship of influenza pandemics to sunspot activity, the faults found in the past analyses are common pitfalls; inattention to analysis reproducibility and robustness assessment are common problems in the sciences, that are unfortunately not noted often enough in review.


2010 ◽  
Vol 331 (7) ◽  
pp. 709-715 ◽  
Author(s):  
K.J. Li ◽  
H.F. Liang

1999 ◽  
Vol 522 (2) ◽  
pp. L153-L156 ◽  
Author(s):  
J. L. Ballester ◽  
R. Oliver ◽  
F. Baudin

Solar Physics ◽  
2005 ◽  
Vol 229 (1) ◽  
pp. 181-198 ◽  
Author(s):  
K. J. Li ◽  
P. X. Gao ◽  
T. W. Su

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.


2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  

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