Real-time processing of I-LOFAR data using signal and image-based artificial intelligence/machine learning methods

Abstract

Solar flares discharge up to 1025J of magnetic energy into the solar atmosphere and are often linked with high-intensity radio emissions known as Solar Radio Bursts (SRBs). SRBs are commonly found in dynamic spectra and are classified into five major spectral classes, ranging from Type I to Type V, based on their form and frequency, and time extent. The automatic detection and classification of such radio bursts is a challenge in solar radio physics due to their heterogeneous form. Near real-time detection and classification of SRBs has become a necessity in recent years due to large data rates generated by advanced radio telescopes such as the LOw-FRequency ARray (LOFAR). This thesis proposes a strategy for developing a very fast image and signal classification system that uses artificial intelligence algorithms to process gigabyte/sec data streams in real-time using the Irish-LOFAR array as its prime data source. Currently, the state-of-the-art systems in this area are falling short of the required performance to process such high-bandwidth data streams. Real-time study of SRBs is crucial for effective solar monitoring because it provides timely information about dynamic solar phenomena, such as flares and coronal mass ejections, allowing us to predict space weather impacts on Earth’s technology and infrastructure. This real-time data helps spacecraft operators, scientists, and public safety officials make informed decisions, validates and refines models of solar behavior, and drives advancements in monitoring technologies, ensuring accurate and proactive responses to solar disturbances