CBPTracker - a web tool to detect and track Solar features from SDO/ AIA images

Abstract

The AIA (Atmospheric Imaging Assembly) instrument, on-board the SDO (Solar Dynamics Observatory) satellite, provides high-resolution and high-cadence solar images since 2010. To extract scientific knowledge from those high-resolution images there is a need for efficient automatic web tools to detect and/or track the coronal bright points (CBPs). Identifying and tracking CBPs is essential for successfully calculate the solar corona rotation rate at different latitudes. Over the last years this topic has been an area of research in solar physics and some effective methods have been developed. The purpose of this work is to design an automatic and near real-time web tool that detects and tracks CBPs on solar images and publishes the results online, thus, allowing search and visualization functionalities to support astrophysicists perform improved solar analysis. The detection process uses a gradient based segmentation algorithm that has proved to provide accurate data about CBPs' dynamics. The paper propose to extend that work by using SunPy and OpenCV in Python, together with the Gradient Path Labeling (GPL) segmentation algorithm. The results obtained display good approximations, when compared with the ones from other authors, and the tool also seems reliable over long testing periods.