Since its commissioning in 2010, SDO has captured one high resolution image every second. A large community effort resulted in the implementation of a series of algorithms for the autonomous detection and tracking of solar events, with outputs available within the Heliophysical Event Knowledgebase (HEK). Machine learning and deep learning have the ability to further leverage on these data, algorithms and database, and to tackle open complex problems. This session will discuss mathematical, statistical, computational, and machine learning techniques for information processing of SDO and solar data, with emphasis on: autonomous prediction of solar events from heterogenous data (magnetogram, EUV imagers, spectrometer), supervised and unsupervised detection, tracking of solar events and features at large and small scales, dimensionality reduction, image enhancement, methods for enriching and mining the HEK e.g. in view of spatio-temporal pattern discovery. Of special interest are techniques that allow for a progress towards reconciling theory with observations.
Plenary speaker: Vincent Barra (Université Clermont-Ferrand)
|09:00||Deep Learning and solar data processing - an introduction with applications||Barra, V||Invited Oral|
| ||Vincent BARRA|
| ||Clermont Auvergne University, LIMOS UMR 6158 CNRS|
| ||Over the past few years, deep learning has become the state-of-the-art technique for many problems: classification, visual recognition, speech recognition, natural language processing or even playing games. The developement of new algorithms as well as the improvment of hardware and computing capacity make these methods able to address new application fields with big data and complex problems. In this talk, I will introduce the main topics that underlie the deep learning paradigm, and then detail four possible applications that solar data processing can benefit from: classification of images or events for segmentation or semantic recognition purposes ; inverse or ill-posed problem solving, particularly focusing on image super resolution ; and data generation using generative adversarial networks.|
|09:30||Sunspot Group Classification using Neural Networks||Maloney, S||Oral|
| ||S. A. Maloney, P. T. Gallagher|
| ||Trinity College Dublin|
| ||Sunspots are the sources of the most extreme and potentially adverse solar events such as flares and CMEs. As such many forecasting systems have been developed to predict these events, a number of which rely on sunspot group classifications. The classifications are manually produced so are subject to human errors and biases. Additionally, as the classifications are only produced on a daily basis this limits the time resolution of some forecasting methods. Further with the imaging cadence of SDO HMI, it would be impossible for a human to produce classifications for every observation. As such the development of an automated classification system would provide many benefits.
Neural networks (NNs) have proven to be powerful tools for solving many complex problems such as classification, regression, and optimisation. In particular, the application of convolutional neural networks (CNNs) to image classification has greatly improved the performance of such systems. The first example of this, in the 1990s, was the identification of handwritten digits from 646 checks an 82% accuracy was achieved. Since then there have been numerous advances in both the network architectures and the underlying components. Recently an accuracy rate of 97.75% was achieved, identifying 1000 classes in 150,000 images for the ILSVRC2017 challenge.
We applied a number of modern CNN architectures to the problem of classifying sunspots groups in SDO HMI observations. The input data consisted of SDO HMI SHARPs magnetograms and the daily McIntosh or Mount Wilson sunspot classifications provided by the USAF/NOAA. The entire dataset (2011-2018) was randomly split into three sets, train, test and validate. The train and test sets were used to optimise the parameters and hyperparameters of the chosen network architectures to achieve optimal performance. Once the all the parameters were fixed the accuracy of the networks were determined using the validation set containing only unseen data. We present the results of this work together with some issues encountered and avenues of further research.|
|09:45||AI-generated magnetogram and EUV image of the Carrington event and the estimation of its Dst value||Lee, H||Oral|
| ||Harim Lee, Y.-J. Moon, Daye Lim, Eunsu Park, and Taeyoung Kim[1,2]|
| ||School of Space Research, Kyung Hee University, Yongin-si 17104, Korea, School of Space Research, Kyung Hee University, Yongin-si 17104, Korea|
| ||We apply an image-to-image translation model, which is a popular deep learning method based on conditional Generative Adversarial Networks (cGANs), to the generation from sunspot drawings to the corresponding magnetograms and EUV images. For this, we train the model using pairs of sunspot drawing from Mount Wilson Observatory (MWO) and their corresponding SDO/HMI magnetogram and SDO/AIA images from 2012 to 2013. We test the model by comparing pairs of actual magnetogram (EUV image) and the corresponding AI-generated one in 2014. Our results show that bipolar structures of the AI-generated magnetograms are consistent with those of the original ones and their unsigned magnetic fluxes (or intensities) are well correlated with those of the original ones with good correlation coefficients. Using this model with the Carrington sunspot drawing, we successfully produce AI-generated magnetogram (EUV image) and estimate unsigned magnetic fluxes. Using several empirical relationships (magnetic flux vs. CME speed, CME speed vs. ICME speed, and ICME speed vs. Dst) in 23 and 24th solar cycle, we conjecture the Dst value of the Carrington event, about -1,670 nT, which is similar to that of Tsurutani et al. (2003).|
|10:00||Solar EUV Spectral Irradiance by Deep Learning||Wright, P||Oral|
| ||Paul Wright, Richard Galvez, Alexandre Szenicer, Rajat Thomas, Meng Jin[5,6], David Fouhey, Mark Cheung[6,8], Andres Munoz-Jaramillo, Graham Mackintosh|
| ||University of Glasgow, Glasgow, United Kingdom, New York University, New York, NY, United States, University of Oxford, Oxford, United Kingdom, University of Amsterdam, Amsterdam, Netherlands, SETI Institute, Mountain View, CA, United States, Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA, United States, University of California, Berkeley, Berkeley, United States, Stanford University, Stanford, CA, United States, Southwest Research Institute Boulder, Boulder, CO, United States, NASA Frontier Development Lab, Mountain View, CA, United States|
| ||Extreme UV (EUV) radiation from the Sun’s transition region and corona is an important driver for the energy balance of the Earth’s thermosphere and ionosphere. To characterise and monitor solar forcing on this system and associated space weather impacts, the EUV Variability Experiment (EVE) instrument onboard NASA’s Solar Dynamics Observatory (SDO) was designed to measure solar spectral irradiance (SSI) in the 0.1 to 105 nm wavelength range. As the result of an electrical short, the MEGS-A component of EVE stopped delivering SSI data in the 5 - 35 nm wavelength range in May 2014. We demonstrate how a Residual Neural Network (ResNet) augmented with a Multi-Layer Perceptron (MLP) can fill this gap using narrowband UV and EUV images from the Atmospheric Imaging Assembly (AIA) on SDO. As a performance benchmark, we also show how our deep learning approach outperforms a physics model based on differential emission measure inversions. This work was performed at NASA’s Frontier Development Lab, a public-private initiative to apply AI techniques to accelerate space science discovery and exploration.
|10:15||Statistically Identifying Systematics from Far-side Acoustic Images||Hess webber, S||Oral|
| ||Shea A. Hess Webber, Junwei Zhao|
| ||Stanford University|
| ||The prediction of solar wind conditions is a critical aspect of space weather forecasting. Solar wind models are highly dependent on the global magnetic field at the solar surface as their inner boundary condition, and the lack of true knowledge of the global field in traditional synoptic maps is a significant problem plaguing space weather forecasting. Currently, only near-side magnetic field observations exist, but far-side magnetic field can be essential for accurate modeling of the Sun’s coronal field and solar wind. Current methods and observations exist that estimate the far-side active region configuration (e.g., helioseismic imaging, flux transport models, far-side EUV observations); however, each of these methods has drawbacks. Our work combines existing techniques with machine-learning and statistical analysis to develop reliable, calibrated far-side magnetic-flux maps in near-real-time, using helioseismic far-side images that are solely dependent on near-side observations. We present an overview of this method and discuss an important progress milestone: We have established a statistical relation between the far-side acoustic images from 7 years of SDO/HMI data and STEREO/EUVI 304 \AA\ data, allowing us to remove some systematics and improve the quality of the far-side acoustic images.|