Automated Coronal Hole Identification via Multi-Thermal Intensity Segmentation

Coronal holes (CH) are regions of open magnetic fields that appear as dark areas in the solar corona due to their low density and temperature compared to the surrounding quiet corona. To date, accurate identification and segmentation of CHs has been a difficult task due to their comparable intensity to local quiet Sun regions. Current segmentation methods typically rely on the use of single EUV passband and magnetogram images to extract CH information. Here, the Coronal Hole Identification via Multi-thermal Emission Recognition Algorithm (CHIMERA) is described, which analyses multi-thermal images from the Atmospheric Image Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) to segment coronal hole boundaries by their intensity ratio across three passbands (171 \AA, 193 \AA, and 211 \AA). The algorithm allows accurate extraction of CH boundaries and many of their properties, such as area, position, latitudinal and longitudinal width, and magnetic polarity of segmented CHs. From these properties, a clear linear relationship was identified between the duration of geomagnetic storms and coronal hole areas. CHIMERA can therefore form the basis of more accurate forecasting of the start and duration of geomagnetic storms

A multi-wavelength analysis of active regions and sunspots by comparison of automatic detection algorithms

YesThe launch of the Solar Dynamics Observatory (SDO) in early 2010 has provided the solar physics community with the most detailed view of the Sun to date. However, this presents new challenges for the analysis of solar data. Currently, SDO sends over 1 terabyte of data per day back to Earth and methods for fast and reliable analysis are more important than ever. This article details four algorithms developed separately at the Universities of Bradford and Glasgow, the Royal Observatory of Belgium and Trinity College Dublin for the purposes of automated detection of solar active regions (ARs) and sunspots at different levels of the solar atmosphere

Framework for near real time feature detection from the atmospheric imaging assembly images of the solar dynamics observatory

The study of the variability of the solar corona and the monitoring of its traditional regions (Coronal Holes, Quiet Sun and Active Regions) are of great importance in astrophysics as well as in view of the Space Weather applications. The Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) provides high resolution images of the sun imaged at different wavelengths at a rate of approximately one every 10 seconds, a great resource for solar monitoring . Today, the process of identifying features and estimating their properties is applied manually in an iterative fashion to verify the detection results. We introduce a complete, automated image-processing pipeline, starting with raw data and ending with quantitative data of high level feature parameters. We implement two multichannel unsupervised algorithms that automatically segments EUV AIA solar images into Coronal Holes, Quiet Sun and Active Regions in near real time. We also develop a method of post processing to deal with fragments in a segmented image by spatial validity based compact clustering. The segmentation results are consistent with well-known algorithms and databases. The parameters extracted from the segments like area closely follow the solar activity pattern. Moreover, the methods developed within the proposed framework are generic enough to allow the study of any solar feature (e.g. Coronal Bright points) provided that the feature can be deduced from AIA images

Prediction of Extreme Ultraviolet Variability Experiment (EVE)/Extreme Ultraviolet Spectro-Photometer (ESP) Irradiance from Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) Images Using Fuzzy Image Processing and Machine Learning

YesThe cadence and resolution of solar images have been increasing dramatically with the launch of new spacecraft such as STEREO and SDO. This increase in data volume provides new opportunities for solar researchers, but the efficient processing and analysis of these data create new challenges. We introduce a fuzzy-based solar feature-detection system in this article. The proposed system processes SDO/AIA images using fuzzy rules to detect coronal holes and active regions. This system is fast and it can handle different size images. It is tested on six months of solar data (1 October 2010 to 31 March 2011) to generate filling factors (ratio of area of solar feature to area of rest of the solar disc) for active regions and coronal holes. These filling factors are then compared to SDO/EVE/ESP irradiance measurements. The correlation between active-region filling factors and irradiance measurements is found to be very high, which has encouraged us to design a time-series prediction system using Radial Basis Function Networks to predict ESP irradiance measurements from our generated filling factors

Automatic Identification of EUV structures on the Sun with a Fuzzy Clustering Algorithm

This technical report describes the first implementation of a Fuzzy c-means (FCM) algorithm for the automatic identification of structures on the Sun based on EUV images and photospheric magnetograms. Before the application of FCM, the AIA 193 Å images and HMI LOS magnetograms acquired by SDO have been pre-processed, and a geometrical approach to correct the limb brightening of EUV images is applied. Then, the images and the magnetograms are analyzed pixel-by-pixel by determining the degree of membership of each pixel to one of clusters, previously defined based on the analysis of a sample training dataset. The routines are written in IDL programming language and will be inserted in the SWELTO pipeline. The work described here was the subject of a Degree Thesis in Physics

Machine learning in solar physics

The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

Detection and Tracking of Coronal Holes in Solar Images

The study of solar activity and its effects on space weather is of great interest to humankind. Whether to study the dynamic of the star itself or the resulting phenomena and associated con-sequences from it, every different feature of the Sun provides valuable data to perform these studies. Features of the Sun are, for the most part, studied individually. However, studying differ-ent events collectively may result in new conclusions and findings that can be of as much interest as the individual studies. The objectives for this dissertation is to complement a Coronal Bright Points (CBPs) tracking algorithm, previously developed by (Pires, 2018), with an additional feature: detection of Coronal Holes (CHs) and classification of CBPs regarding whether they are located inside or outside of CHs. The proposed methodology is fully performed in Python language. Different image pro-cessing operations are applied in order to obtain a good detection result. The pre-processing stage involves an automatic image intensity normalization. The CHs detection uses a simple blur-ring before a fixed-value threshold segmentation. A last post-processing step includes performing adjustments to the detection results, using a closing morphologic operator, filling holes and an object detection. The data gathered by both tools is at the end consolidated, so that a result on the classifi-cation of each CBP is obtained and lastly added to the database