Authors: Sailee Sawant (The University of Alabama in Huntsville), Gang Li, Meng Jin (Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA 94304, USA)

Active regions (ARs) on the Sun predominantly act as “hotspots” for solar activity, exhibiting intricate magnetic field structures and intense energy outbursts. Solar flares and coronal mass ejections (CMEs) originating from complex ARs can potentially cause disruptive space weather phenomena, including geomagnetic storms and solar energetic particle (SEP) events, which can impact various societal and technological systems on Earth. Additionally, increased radiation levels can pose serious risks to the health of astronauts and the functionality of sensitive instruments in space. Therefore, state-of-the-art forecasting models to accurately predict space weather conditions are needed. This research aims to develop a physics-based operational SEP forecast model, SEPCaster, for the energetic particle radiation environment in the inner Solar System and Earth’s magnetosphere.

SEPCaster is based on two advanced research models: the Alfvén Wave Solar Model (AWSoM) and the improved Particle Acceleration and Transport (iPATH) model. The AWSoM model is incorporated to generate the background solar wind and CMEs, and the iPATH model is employed to track the acceleration and transportation of SEPs. SEPCaster operates in two distinct modes: an automatic forecasting mode and a user-interactive mode. In the automated mode, SEPCaster runs with minimal human interaction, while in the interactive mode, users can modify the inputs and analyze specific events in greater detail.

This presentation focuses on a new Python-based automated module for identifying and characterizing ARs. We start by using real-time National Solar Observatory/Global Oscillation Network Group (NSO/GONG) magnetograms as our raw inputs and apply an image segmentation technique to detect regions of interest (ROIs) with positive and negative polarities. Next, we implement and refine a hierarchical clustering algorithm to identify potential ARs from the detected ROIs. We then calculate a set of parameters to characterize these ARs, including our newly defined boundary- and area-based AR complexity indices. With these parameters, we also compute potential CME eruption speeds, which will be incorporated into SEPCaster to drive the CME shocks. We will discuss the results of our test cases, which are based on NSO/GONG magnetograms acquired from 2006 to 2017.