Note : I am not allowing to anyone to see full project codes because it is a confidential research work under the government of INDIA.
Solar flares are a common occurrence that brighten the Sun's immediate area while ejecting massive amounts of energy and charged particles into the surrounding space. This excess energy is first held in magnetic flux loops coming from active solar areas before being released. It can be devastating to have such a significant event in Earth's line of sight. Additionally, similar occurrences a larger space weather effect. The development of our knowledge and observations u sing solar astronomy, which allows us to analyse the Sun in great detail, we can create a better model and reliably correlate them. However, current models can't quite capture the right triggering mechanism and potential flare intensity.
A statistical study based on the many observational data from the Sun that are now accessible can open up fresh perspectives on this issue. Machine learning analyses, especially the more in-depth ones, has a great deal of potential to be used in this way. We want to investigate the same in the current report. To determine the capacity of various deep neural networks to forecast solar flares, we want to compare them with the observational data that is currently available and then further improve them using theoretical inputs.