Preprocessing Solar Images while Preserving their Latent Structure

Telescopes such as the Atmospheric Imaging Assembly aboard the Solar Dynamics Observatory, a NASA satellite, collect massive streams of high resolution images of the Sun through multiple wavelength filters. Reconstructing pixel-by-pixel thermal properties based on these images can be framed as an ill-posed inverse problem with Poisson noise, but this reconstruction is computationally expensive and there is disagreement among researchers about what regularization or prior assumptions are most appropriate. This article presents an image segmentation framework for preprocessing such images in order to reduce the data volume while preserving as much thermal information as possible for later downstream analyses. The resulting segmented images reflect thermal properties but do not depend on solving the ill-posed inverse problem. This allows users to avoid the Poisson inverse problem altogether or to tackle it on each of $\sim$10 segments rather than on each of $\sim$10$^7$ pixels, reducing computing time by a factor of $\sim$10$^6$. We employ a parametric class of dissimilarities that can be expressed as cosine dissimilarity functions or Hellinger distances between nonlinearly transformed vectors of multi-passband observations in each pixel. We develop a decision theoretic framework for choosing the dissimilarity that minimizes the expected loss that arises when estimating identifiable thermal properties based on segmented images rather than on a pixel-by-pixel basis. We also examine the efficacy of different dissimilarities for recovering clusters in the underlying thermal properties. The expected losses are computed under scientifically motivated prior distributions. Two simulation studies guide our choices of dissimilarity function. We illustrate our method by segmenting images of a coronal hole observed on 26 February 2015

Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.

This dissertation addresses two different problems: 1) coronal loop detection from solar images: and 2) salient contour group extraction from cluttered images. In the first part, we propose two different solutions to the coronal loop detection problem. The first solution is a block-based coronal loop mining method that detects coronal loops from solar images by dividing the solar image into fixed sized blocks, labeling the blocks as Loop or Non-Loop , extracting features from the labeled blocks, and finally training classifiers to generate learning models that can classify new image blocks. The block-based approach achieves 64% accuracy in IO-fold cross validation experiments. To improve the accuracy and scalability, we propose a contour-based coronal loop detection method that extracts contours from cluttered regions, then labels the contours as Loop and Non-Loop , and extracts geometric features from the labeled contours. The contour-based approach achieves 85% accuracy in IO-fold cross validation experiments, which is a 20% increase compared to the block-based approach. In the second part, we propose a method to extract semi-elliptical open curves from cluttered regions. Our method consists of the following steps: obtaining individual smooth contours along with their saliency measures; then starting from the most salient contour, searching for possible grouping options for each contour; and continuing the grouping until an optimum solution is reached. Our work involved the design and development of a complete system for coronal loop mining in solar images, which required the formulation of new Gestalt perceptual rules and a systematic methodology to select and combine them in a fully automated judicious manner using machine learning techniques that eliminate the need to manually set various weight and threshold values to define an effective cost function. After finding salient contour groups, we close the gaps within the contours in each group and perform B-spline fitting to obtain smooth curves. Our methods were successfully applied on cluttered solar images from TRACE and STEREO/SECCHI to discern coronal loops. Aerial road images were also used to demonstrate the applicability of our grouping techniques to other contour-types in other real applications

The Pinhole/Occulter Facility

Scientific objectives and requirements are discussed for solar X-ray observations, coronagraph observations, studies of coronal particle acceleration, and cosmic X-ray observations. Improved sensitivity and resolution can be provided for these studies using the pinhole/occulter facility which consists of a self-deployed boom of 50 m length separating an occulter plane from a detector plane. The X-ray detectors and coronagraphic optics mounted on the detector plane are analogous to the focal plane instrumentation of an ordinary telescope except that they use the occulter only for providing a shadow pattern. The occulter plane is passive and has no electrical interface with the rest of the facility

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

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

Magnetic reconnection in small and large scales on the sun

Almost all solar activities observed are related to the solar magnetic field, especially the topological restructuring of magnetic fields, the so-called magnetic reconnection in solar physics. They are occurring at different scales related to various phenomena, during minimum or maximum of the solar cycle, from the photosphere to the solar corona. For small-scale activities, type II spicules which display high velocity upflows observed at the limb, have the most possible underlying driving mechanism of magnetic reconnection. A set of tools is developed for detecting small-scale solar magnetic cancellations and the disk counterpart of type II spicules (the so-called Rapid Blueshifted Excursions, RBEs), using line-of-sight photospheric magnetograms and chromospheric spectroscopic observations, respectively. These tools are further employed to analyze coordinated observation using the Interferometric BIdimensional Spectrometer at the Dunn Solar Telescope of the National Solar Observatory and Hinode. The statistical properties of magnetic cancellations and RBEs are presented and their correlation is explored using this data set. For large-scale activities, recent high resolution extreme-ultraviolet observation from the Solar Dynamics Observatory (SDO) is able to diagnose the plasma around current sheet, the key role of magnetic reconnection during energetic solar flares. Supra-arcade downflows (SADs) have been frequently observed during the gradual phase of flares near the limb. In coronal emission lines sensitive to flaring plasma, they appear as tadpole-like dark voids against the diffuse fan-shaped “haze” above the well-defined flare arcade and flow toward the arcade. Several selected SADs from two flare events are carefully studied. Their differential emission measures (DEMs) and DEM-weighted temperatures are calculated using data obtained by the Atmospheric Imaging Assembly onboard SDO. This analysis corroborates that SADs are density depletion associated with a substantial decrease in DEM. This depression in DEM rapidly recovers in the wake of the SADs studied, generally within a few minutes, suggesting that they are discrete features. In addition, SADs in one event are found to be spatio-temporally associated with the successive formation of post-flare loops along the flare arcade

Solar Jet Hunter: a citizen science initiative to identify coronal jets in EUV data sets

Context. Solar coronal jets seen in EUV are ubiquitous on the Sun, have been found in and at the edges of active regions, at the boundaries of coronal holes, and in the quiet Sun. Jets have various shapes, sizes, brightness, velocities and duration in time, which complicates their detection by automated algorithms. So far, solar jets reported in the Heliophysics Event Knowledgebase (HEK) have been mostly reported by humans looking for them in the data, with different levels of precision regarding their timing and positions. Aims. We create a catalogue of solar jets observed in EUV at 304 {\AA} containing precise and consistent information on the jet timing, position and extent. Methods. We designed a citizen science project, "Solar Jet Hunter", on the Zooniverse platform, to analyze EUV observations at 304 {\AA} from the Solar Dynamic Observatory/Atmospheric Imaging Assembly (SDO/AIA). We created movie strips for regions of the Sun in which jets have been reported in HEK and ask the volunteers to 1) confirm the presence of at least one jet in the data and 2) report the timing, position and extent of the jet. Results. We report here the design of the project and the results obtained after the analysis of data from 2011 to 2016. 365 "coronal jet" events from HEK served as input for the citizen science project, equivalent to more than 120,000 images distributed into 9,689 "movie strips". Classification by the citizen scientists resulted with only 21% of the data containing a jet, and 883 individual jets being identified. Conclusions. We demonstrate how citizen science can enhance the analysis of solar data with the example of Solar Jet Hunter. The catalogue of jets thus created is publicly available and will enable statistical studies of jets and related phenomena. This catalogue will also be used as a training set for machines to learn to recognize jets in further data sets

IMPLEMENTASI ALGORITMA EXTREME LEARNING MACHINE PADA PREDIKSI AKTIVITAS BADAI GEOMAGNETIK

Badai geomagnetik merupakan gangguan yang terjadi di magnetosfer bumi, akibat adanya aktivitas dari matahari. Dalam rangka peringatan dini, Lembaga Penerbangan dan Antariksa Nasional (LAPAN) di Indonesia memiliki kegiatan rutin untuk memprediksi kemungkinan terjadinya badai tersebut dalam rentang waktu 24 jam ke depan. Namun pada tahun 2015, hasil prediksi badai geomagnetik yang dilakukan secara manual oleh LAPAN hanya mendapatkan akurasi sebesar 57,14%. Oleh karena itu, penelitian ini mengusulkan pemanfaatan metode Extreme Learning Machine (ELM) dalam melakukan prediksi badai geomagnetik, dengan tujuan untuk mendapatkan akurasi yang lebih baik. Data penelitian yang digunakan meliputi data coronal hole, coronal mass ejection, solar wind dan indeks Dst pada tahun 2011 hingga 2016. Hasil penelitian ini menunjukkan bahwa algoritma ELM memiliki tingkat akurasi yang lebih besar dalam memprediksi badai geomagnetik tahun 2015, dengan perolehan nilai 57,80822%. Meskipun memiliki selisih akurasi yang kecil, namun pemanfaatan ELM ini dapat membantu prediksi badai geomagnetik secara otomatis. Secara umum, algoritma ELM yang dibangun dalam penelitian ini memiliki nilai rata-rata akurasi prediksi tertinggi sebesar 69,9055%.---------- The geomagnetic storm is a disturbance that occurs in the earth’s magnetosphere, as the result of the activity of the sun. In case for early warning, National Institute of Aeronautics and Space Agency (LAPAN) in Indonesia has a routine activity to predict the probability of geomagnetic storm appearance for the next 24 hours. But in 2015, the geomagnetic storm prediction results are done manually just managed to get the accuracy of 57.14%. Therefore, this research proposes the utilization method of Extreme Learning Machine (ELM) for geomagnetic storm prediction, in order to get better accuracy. Research data that used include data on coronal holes, coronal mass ejection, solar wind and the Dst index from 2011 to 2016. The results of this research show that the ELM algorithm has a greater accuracy in prediction the 2015 geomagnetic storm activity, with the acquisition of 57.80822% value. Despite the difference in accuracy is small, but the utilization of ELM can help predicting geomagnetic storm automatically. In general, the ELM algorithm built in this research have the average value of the highest prediction accuracy of 69.9055%

PREDIKSI SOLAR FLARES MENGGUNAKAN PRODUK DATA VECTOR MAGNETIC SDO/HMI DAN RANDOM FERNS

Solar Flares (SFs) merupakan letusan energi tiba-tiba yang disebabkan oleh kekusutan, persilangan, atau penataan ulang garis medan magnet di dekat bintik matahari. Fenomena ini juga diketahui sebagai letusan paling kuat di tata surya yang sering kali memberi pengaruh buruk bagi cuaca ruang angkasa. Oleh karena itu, banyak peneliti dengan beragam pendekatan berusaha memprediksi akan kemunculannya. Salah satu yang berperan penting pada upaya prediksi ini adalah Instrumen Helioseismic and Magnetic Imager (HMI) pada Solar Dynamic Observatory (SDO) yang secara terus menerus mengamati full-disk photospheric vector magnetic field dari luar angkasa di saat kebanyakan penelitian berbasis pada observasi dari dalam bumi. Berbekal data flux SFs yang direkam oleh Instrumen X-ray Sensors (XRS) pada Geostationary Operational Environmental Satellite (GOES) dan dipetakan dengan data vector magnetic berdasarkan Active Region (AR) Numbers, pada rentang 01 Mei 2010 sampai 10 Mei 2020, penelitian ini mengajukan model prediksi multiclass dan binary SFs menggunakan Algoritma Random Ferns (RFe) dan Teknik Oversampling. Implementasi Naive Bayesian Classification pada RFe sendiri diketahui optimal menangani banyak features yang merupakan kunci untuk meningkatkan tingkat klasifikasi. Sementara Teknik Oversampling digunakan untuk menyeimbangkan kelas minor pada populasi. Hasil dari penelitian ini menunjukkan bahwa model prediksi SFs menggunakan RFe dapat mengungguli beberapa aspek penelitian terdahulu. Adapun nilai rata-rata tertinggi sensitivity/recall, precision, dan TSS multiclass SFs yang diraih penelitian ini secara berturut-turut adalah 74,4%, 50,3%, dan 58,7%. Sementara nilai rata-rata tertinggi sensitivity/recall, precision, dan TSS binary SFs yang diraih penelitian ini secara berturut-turut adalah 87,7%, 77,7%, dan 72,8%. Solar Flares (SFs) are sudden bursts of energy caused by tangling, crossing, or reorganizing of magnetic field lines near sunspots. This phenomenon is also known to be the most powerful eruption in the solar system which often yield adverse impact on space weather. Therefore, many researchers with various approaches try to predict its occurrence. One that plays an important role in this prediction effort is the Helioseismic and Magnetic Imager (HMI) Instrument on the Solar Dynamic Observatory (SDO) which continuously observes the full-disk photospheric vector magnetic field from space while most research is ground-based observations. By using SFs flux data recorded by the X-ray Sensors (XRS) Instrument on the Geostationary Operational Environmental Satellite (GOES) and mapped with vector magnetic data based on the Active Region (AR) Numbers, in the range 01 May 2010 to 10 May 2020, this study proposes a multiclass and binary SFs prediction model using Random Ferns (RFe) Algorithm and Oversampling Technique. The implementation of the Naive Bayesian Classification on RFe itself is known to be optimal in handling many features which are the key to increase the classification rate. While the Oversampling Technique is used to balance the minor classes in the population. The results of this study indicate that the SFs prediction model using RFe can outperform several aspects of previous research. The highest average scores for sensitivity/recall, precision, and TSS multiclass SFs achieved in this study were 74.4%, 50.3%, and 58.7%, respectively. Meanwhile, the highest average values for sensitivity/recall, precision, and TSS binary SFs achieved in this study were 87.7%, 77.7%, and 72.8%, respectively