| Abstract : | Understanding the coronal heating problem and predicting the space weather are two of the most outstanding challenges in front of heliophysicists. While it was long realized that radio observations can play a crucial role in studying these problems, the imaging dynamic range in the past was not sufficient enough to progress in these fields. New generation instruments like the Murchison Widefield Array, however, have the capability to record data at a sensitivity, resolution and bandwidth sufficient to start tackling these issues from a radio perspective. However this comes at the cost of huge data volumes making manual analysis of the data impractical. In this thesis, we have developed and implemented an ingenious algorithm to produce high dynamic range solar radio images in a completely automated manner. The pipeline, named AIRCARS, has been successfully tested on solar data spanning a wide range of solar conditions, and regularly produced images with dynamic range typically one to two orders of magnitude greater than possible earlier. The high dynamic range imaging capability allowed us to estimate the magnetic field entrained inside a coronal mass ejection (CME) by modelling the gyrosynchrotron emission from it. While this emission is predicted to be present in all CMEs, in the past it has only been detected in only four fast CMEs. Thus our detection and modelling of this emission from a weak and rather unremarkable CME shows the potential this technique has for space weather research and applications. The high dynamic range images produced by AIRCARS also allowed us to successfully detect very weak transient emissions throughout the quiet sun. The flux density of these transients is about three orders of magnitude lower than earlier studies, and satisfy multiple necessary conditions for them to be the radio counterparts of nanoflares, hypothesised to be responsible for coronal heating. |
