Prateek Boga
Understanding the working of the Sun has been intriguing to humanity for ages. After tons of turmoil and years of work, humans acquired the knowledge that is being processed now. To add a small blip to that pile, the research work has been done to understand the relation between Sunspot Area and Solar activity. Sunspots tend to develop on those regions of the surface where there is extensive stress in magnetic fields and are relatively regions of lower temperatures on the hot Sun’s surface. The solar activities, including Solar Flares that can be defined as a sudden explosion of energy caused by tangling, crossing, or reorganizing of magnetic field lines near Sunspots, Coronal Mass Ejections (CMEs) that are associated with the Solar Flares, are large expulsions of plasma and magnetic field from the Sun’s corona, etc. originate from the Sunspots. One of the properties of magnetic field lines is that they don't intersect with each other, and the magnetic strength is stronger where they are close together. Hence, it was proposed that the Sunspot Area (SSA) is inversely proportional to solar activity. Thus, a detailed correlative study was performed with a variety of solar activity parameters like CME width, Number of Solar Flares, Solar Flare Index, Linear Speed of CMEs, 2nd order initial and final Speeds of CMEs, and Total Solar Irradiance. Then, the graphs were plotted and Curve-Fitted for different Sunspot Number ranges and Magnetic Flux Differences and analyzed. The results supported the hypothesis with an accuracy of 0.71.
This poster portrays the work of a team of Space enthusiasts who worked on our star, The Sun. We set off to search a relation between the activities of the Sun and one of its parameters. The work and results achieved by us were never done before. Read more to know!
Activities on our Sun
There are numerous activities that take place inside, on and over the surface. These activities are mainly due to the complex magnetic field of the sun. The Sun’s magnetic poles reverse every 22 years (known as solar cycle). During these cycles, activities like Sunspot formation, Solar Flares, Coronal Mass Ejections (CMEs) etc are witnessed. Due to extreme field strengths at some locations, dark spots with lower temperatures are formed, known as Sunspots. Parameters associated with these are Sunspot Number (SSN) and Sunspot Area (SSA) (Self-explanatory).
What did we do and why?
We proposed a hypothesis- ‘Solar Activities are inversely proportional to SSA’. The theory is based on basic properties of magnetic field lines-they do not intersect and closer lines correspond to high strength. This suggests that for a lesser SSA, we will have high Solar Activity. To verify our hypothesis, we utilized the data of parameters of CMEs, Solar Flares and compared it with SSA.
How did we do that?!
We set off to search for the data of the parameters (table). Data from credible sources like NASA, ESA and published papers were found, but were in a raw form i.e. it required certain processing before proceeding. The parameters depend upon other parameters as well, like SSN, magnetic flux (MF), and not just SSA. To understand the relation with SSA irrespective of those parameters, the processed data had to be refined. Using computational methods, we refined the data in two stages. However, due to lack of data after refining, we had to settle for a dataset which was affected by MF values. To circumvent this problem, we tracked the trend of the change in nature of relation with respect to effects of MF on the relation and the changes in SSN.
Hence, we took the refined data for all the 6 parameters and its corresponding SSA data and plotted for various MF differences and various SSN. We plotted this data and tried to get the best possible relation between the parameters. To indicate the type of relation between these, we took help of Spearman coefficient. This value describes the nature of relation i.e. if the value is between -1 and 0, it describes an inverse relation and a direct relation for value between 0 and 1.
What did we infer? Were we right?
Indeed, we found that as the effects of MF increased on the data set, the relation turned to a direct one (values shown in table). This clearly suggests, upon extrapolating the relation for data with no effects of MF, the relation must be an inverse relation which bolsters our hypothesis. 10 out of 14 trends supported our hypothesis (accuracy 71.42%). In our results, we also found strange behaviour for certain instances. The reason for this is lack of data after the refining process.