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A web tool to detect and track Solar features from SDO images
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Resumo(s)
Coronal bright points (CBPs) are useful features that can be used to calculate solar rotation even when no active regions are present. Unlike active regions, CBPs are dis-tributed at all latitudes on the solar disk and its lifetime varies from less than an hour to a few days.
Identifying and tracking CBPs are the main keys to successfully calculate the Solar corona rotation profile for different latitudes. Over the last years this topic has been an area of research in solar astronomy and some effective methods have been developed.
The purpose of this dissertation was to design a web tool that retrieves, prepro-cesses, detects and tracks CBPs on solar images and that allows search and visualization of CBPs and solar information from a database, helping astrophysicists on their solar analysis.
The detection uses a gradient based segmentation algorithm that has proved to provide accurate data about CBPs’ dynamics.
It was developed a website to visualize the results, hosted by SPINLab. The track-ing from 480 images confirmed to be consistent within the expected when comparing with other authors’ work.
This topic was motivated by the astrophysicists need for a near to real-time tool that allows the most recent data, as well as archive with historical data, concerning the Solar corona rotation to be processed just a few minutes after the image being captured by Nasa’s Atmospheric Imaging Assembly on board of the Solar Dynamic Observatory.
Descrição
Palavras-chave
coronal bright points feature tracking Gradient Path Labelling (GPL) image processing Python segmentation algorithms