It’s over!

Sorry for taking a month off from blogging. I was very busy writing my dissertation. I finally submitted the dissertation report on this Friday 6th September. Although tiresome and frustrating at times, this is an experience I would remember for days to come.

My project was on an inter-disciplinary problem – to develop a visualizer for scientists to analyze sunspot appearance pattern. I am fortunate being supervised by Dr. John Brooke, who has worked with astrophysicists on solar problems for a long time. I think I did a good job in this dissertation. But, I could not have made it so much better unless my supervisor guided me through.

Several times, I was completely clueless about how to proceed, and he directed me to find alternate ways or think deeper on my hypothesis. He kept encouraging me, saying this is an experience a researcher has to go through. The highs and lows are part and parcel of every project, specially research where there is no guarantee that the actual output would match with expected result.

The project topic is to build a visualizer program which can simulate the actual sunspot appearance pattern. For any simulation, it is imperative for the programmer to understand the physical process. During this project, I learnt many astronomical details regarding solar activities, specially sunspots. The exact details about sunspot appearance is still not known. Official prediction about sunspot appearance in recent times had to be revised multiple times. More interestingly, this year solar activities are supposed to be at its peak. However, solar activities so far have been below average.

The other challenge is to extract information through which we can compare the simulation to the real output. This was the toughest part of the project. I approximated the real dataset to form a model of stochastic resonance. Basic stochastic resonance model operates with three inputs – signal, noise and threshold.

In this case, signal value is lower than the threshold value. So, it alone could not cross the threshold barrier and give any output. But, adding it with noise, the compound signal crosses threshold barrier at times, giving random output, but also adhering to base signal pattern. The reason of choosing this model lies in the pattern of real sunspot appearance.

Sunspots randomly appear on solar surface. But, if the trend of sunspot is analyzed for a long period, it seems they follow a cyclic pattern. For example, if we plot sunspot count with time, the plot forms a sinusoidal pattern with period of about 11 years. That means, the duration between two successive solar maximas and two solar minimas is about 11 years.
It is assumed that this regular pattern is caused by the base solar signal, and the randomness of sunspot appearance is caused by the noise involved in this process. With stochastic resonance model, the simulation works well to replicate approximated real sunspot dataset. Documenting all these into dissertation report is another painstaking task though.

Masters dissertation report demands high standard. To collate everything related to the project in a coherent form is tough, specially with stringent time constraint. I forgot how many times I modified my original writing. It’s always helpful to proof-read the report by somebody else, specially by your supervisor. On the first draft, I got so many comments, that it reminds of Indian roads, where potholes are easier to find than the actual road. 🙂

At the end, you would feel disgusted to look at the same report over and over again, and yet discover so many loopholes. Fortunately, it’s all over for me. The result is due to be announced on November. Hope my report gets good feedback!