Solar Dynamics Observatory (SDO)

Context.The Extreme Ultraviolet Imager (EUI) is part of the remote sensing instrument package of the ESA/NASA Solar Orbiter mission that will explore the inner heliosphere and observe the Sun from vantage points close to the Sun and out of the ecliptic. Solar Orbiter will advance the “connection science” between solar activity and the heliosphere.Aims.With EUI we aim to improve our understanding of the structure and dynamics of the solar atmosphere, globally as well as at high resolution, and from high solar latitude perspectives.Methods.The EUI consists of three telescopes, the Full Sun Imager and two High Resolution Imagers, which are optimised to image in Lyman-αand EUV (17.4 nm, 30.4 nm) to provide a coverage from chromosphere up to corona. The EUI is designed to cope with the strong constraints imposed by the Solar Orbiter mission characteristics. Limited telemetry availability is compensated by state-of-the-art image compression, onboard image processing, and event selection. The imposed power limitations and potentially harsh radiation environment necessitate the use of novel CMOS sensors. As the unobstructed field of view of the telescopes needs to protrude through the spacecraft’s heat shield, the apertures have been kept as small as possible, without compromising optical performance. This led to a systematic effort to optimise the throughput of every optical element and the reduction of noise levels in the sensor.Results.In this paper we review the design of the two elements of the EUI instrument: the Optical Bench System and the Common Electronic Box. Particular attention is also given to the onboard software, the intended operations, the ground software, and the foreseen data products.Conclusions.The EUI will bring unique science opportunities thanks to its specific design, its viewpoint, and to the planned synergies with the other Solar Orbiter instruments. In particular, we highlight science opportunities brought by the out-of-ecliptic vantage point of the solar poles, the high-resolution imaging of the high chromosphere and corona, and the connection to the outer corona as observed by coronagraphs.

The Solar Orbiter EUI instrument: The Extreme Ultraviolet Imager

We attempt to forecast M- and X-class solar flares using a machine-learning algorithm, called support vector machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first instrument to continuously map the full-disk photospheric vector magnetic field from space. Most flare forecasting efforts described in the literature use either line-of-sight magnetograms or a relatively small number of ground-based vector magnetograms. This is the first time a large data set of vector magnetograms has been used to forecast solar flares. We build a catalog of flaring and non-flaring active regions sampled from a database of 2071 active regions, comprised of 1.5 million active region patches of vector magnetic field data, and characterize each active region by 25 parameters. We then train and test the machine-learning algorithm and we estimate its performances using forecast verification metrics with an emphasis on the true skill statistic (TSS). We obtain relatively high TSS scores and overall predictive abilities. We surmise that this is partly due to fine-tuning the SVM for this purpose and also to an advantageous set of features that can only be calculated from vector magnetic field data. We also apply a feature selection algorithm to determine which of our 25 features are useful for discriminating between flaring and non-flaring active regions and conclude that only a handful are needed for good predictive abilities.

Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm

NASA’s Solar Dynamics Observatory (SDO) mission gathers 1.4 terabytes of data each day from its geosynchronous orbit in space. SDO data includes images of the Sun captured at different wavelengths, with the primary scientific goal of understanding the dynamic processes governing the Sun. Recently, end-to-end optimized artificial neural networks (ANN) have shown great potential in performing image compression. ANN-based compression schemes have outperformed conventional hand-engineered algorithms for lossy and lossless image compression. We have designed an ad-hoc ANN-based image compression scheme to reduce the amount of data needed to be stored and retrieved on space missions studying solar dynamics. In this work, we propose an attention module to make use of both local and non-local attention mechanisms in an adversarially trained neural image compression network. We have also demonstrated the superior perceptual quality of this neural image compressor. Our proposed algorithm for compressing images downloaded from the SDO spacecraft performs better in rate-distortion tradeoff than the popular currently-in-use image compression codecs such as JPEG and JPEG2000. In addition we have shown that the proposed method outperforms state-of-the art lossy transform coding compression codec, i.e., BPG.

Attention-Based Generative Neural Image Compression on Solar Dynamics Observatory

We present a new model for the probability that the Disturbance storm time (Dst) index exceeds -100 nT, with a lead time between 1 and 3 days. $Dst$ provides essential information about the strength of the ring current around the Earth caused by the protons and electrons from the solar wind, and it is routinely used as a proxy for geomagnetic storms. The model is developed using an ensemble of Convolutional Neural Networks (CNNs) that are trained using SoHO images (MDI, EIT and LASCO). The relationship between the SoHO images and the solar wind has been investigated by many researchers, but these studies have not explicitly considered using SoHO images to predict the $Dst$ index. This work presents a novel methodology to train the individual models and to learn the optimal ensemble weights iteratively, by using a customized class-balanced mean square error (CB-MSE) loss function tied to a least-squares (LS) based ensemble. The proposed model can predict the probability that Dst<-100 nT 24 hours ahead with a True Skill Statistic (TSS) of 0.62 and Matthews Correlation Coefficient (MCC) of 0.37. The weighted TSS and MCC from Guastavino et al. (2021) is 0.68 and 0.47, respectively. An additional validation during non-Earth-directed CME periods is also conducted which yields a good TSS and MCC score. 

Probabilistic Prediction of <i>Dst</i> Storms One‐Day‐Ahead Using Full‐Disk SoHO Images

Solar activity plays a quintessential role in influencing the interplanetary
medium and space-weather around the Earth. Remote sensing instruments onboard
heliophysics space missions provide a pool of information about the Sun's
activity via the measurement of its magnetic field and the emission of light
from the multi-layered, multi-thermal, and dynamic solar atmosphere. Extreme UV
(EUV) wavelength observations from space help in understanding the subtleties
of the outer layers of the Sun, namely the chromosphere and the corona.
Unfortunately, such instruments, like the Atmospheric Imaging Assembly (AIA)
onboard NASA's Solar Dynamics Observatory (SDO), suffer from time-dependent
degradation, reducing their sensitivity. Current state-of-the-art calibration
techniques rely on periodic sounding rockets, which can be infrequent and
rather unfeasible for deep-space missions. We present an alternative
calibration approach based on convolutional neural networks (CNNs). We use
SDO-AIA data for our analysis. Our results show that CNN-based models could
comprehensively reproduce the sounding rocket experiments' outcomes within a
reasonable degree of accuracy, indicating that it performs equally well
compared with the current techniques. Furthermore, a comparison with a standard
"astronomer's technique" baseline model reveals that the CNN approach
significantly outperforms this baseline. Our approach establishes the framework
for a novel technique to calibrate EUV instruments and advance our
understanding of the cross-channel relation between different EUV channels.

/pdf/multi-channel-auto-calibration-for-the-atmospheric-imaging-19yp9twbqz.pdf

Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning

Solar activity plays a quintessential role in influencing the interplanetary medium and space-weather around the Earth. Remote sensing instruments onboard heliophysics space missions provide a pool of information about the Sun's activity via the measurement of its magnetic field and the emission of light from the multi-layered, multi-thermal, and dynamic solar atmosphere. Extreme UV (EUV) wavelength observations from space help in understanding the subtleties of the outer layers of the Sun, namely the chromosphere and the corona. Unfortunately, such instruments, like the Atmospheric Imaging Assembly (AIA) onboard NASA's Solar Dynamics Observatory (SDO), suffer from time-dependent degradation, reducing their sensitivity. Current state-of-the-art calibration techniques rely on periodic sounding rockets, which can be infrequent and rather unfeasible for deep-space missions. We present an alternative calibration approach based on convolutional neural networks (CNNs). We use SDO-AIA data for our analysis. Our results show that CNN-based models could comprehensively reproduce the sounding rocket experiments' outcomes within a reasonable degree of accuracy, indicating that it performs equally well compared with the current techniques. Furthermore, a comparison with a standard "astronomer's technique" baseline model reveals that the CNN approach significantly outperforms this baseline. Our approach establishes the framework for a novel technique to calibrate EUV instruments and advance our understanding of the cross-channel relation between different EUV channels.

/pdf/multi-channel-auto-calibration-for-the-atmospheric-imaging-s3fhguat9i.pdf

Luiz F. G. dos Santos

Papers

Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning

Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning