arXiv subjects

Haimin Wang

Publications and source records attributed to Haimin Wang.

At least 55 records · Page 3Linked to original sources

arXiv2024

On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee

Model compression is a crucial part of deploying neural networks (NNs), especially when the memory and storage of computing devices are limited in many applications. This paper focuses on two model compression techniques: low-rank approximation and weight pruning in neural networks, which are very popular nowadays. However, training NN with low-rank approximation and weight pruning always suffers significant accuracy loss and convergence issues. In this paper, a holistic framework is proposed for model compression from a novel perspective of nonconvex optimization by designing an appropriate objective function. Then, we introduce NN-BCD, a block coordinate descent (BCD) algorithm to solve the nonconvex optimization. One advantage of our algorithm is that an efficient iteration scheme can be derived with closed-form, which is gradient-free. Therefore, our algorithm will not suffer from vanishing/exploding gradient problems. Furthermore, with the Kurdyka-Łojasiewicz (KŁ) property of our objective function, we show that our algorithm globally converges to a critical point at the rate of O(1/k), where k denotes the number of iterations. Lastly, extensive experiments with tensor train decomposition and weight pruning demonstrate the efficiency and superior performance of the proposed framework. Our code implementation is available at https://github.com/ChenyangLi-97/NN-BCD

cs.LG↗

arXiv2024

Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network

Image super-resolution has been an important subject in image processing and recognition. Here, we present an attention-aided convolutional neural network (CNN) for solar image super-resolution. Our method, named SolarCNN, aims to enhance the quality of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO). The ground-truth labels used for training SolarCNN are the LOS magnetograms collected by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). Solar ARs consist of strong magnetic fields in which magnetic energy can suddenly be released to produce extreme space weather events, such as solar flares, coronal mass ejections, and solar energetic particles. SOHO/MDI covers Solar Cycle 23, which is stronger with more eruptive events than Cycle 24. Enhanced SOHO/MDI magnetograms allow for better understanding and forecasting of violent events of space weather. Experimental results show that SolarCNN improves the quality of SOHO/MDI magnetograms in terms of the structural similarity index measure (SSIM), Pearson's correlation coefficient (PCC), and the peak signal-to-noise ratio (PSNR).

astro-ph.SR↗

arXiv2024

Episodic energy release during the main- and post-impulsive phase of a solar flare

When and where the magnetic field energy is released and converted in eruptive solar flares remains an outstanding topic in solar physics. To shed light on this question, here we report multi-wavelength observations of a C9.4-class eruptive limb flare that occurred on 2017 August 20. The flare, accompanied by a magnetic flux rope eruption and a white light coronal mass ejection, features three post-impulsive X-ray and microwave bursts immediately following its main impulsive phase. For each burst, both microwave and X-ray imaging suggest that the non-thermal electrons are located in the above-the-loop-top region. Interestingly, contrary to many other flares, the peak flux of the three post-impulsive microwave and X-ray bursts shows an increase for later bursts. Spectral analysis reveals that the sources have a hardening spectral index, suggesting a more efficient electron acceleration into the later post-impulsive bursts. We observe a positive correlation between the acceleration of the magnetic flux rope and the non-thermal energy release during the post-impulsive bursts in the same event. Intriguingly, different from some other eruptive events, this correlation does not hold for the main impulse phase of this event, which we interpret as energy release due to the tether-cutting reconnection before the primary flux rope acceleration occurs. In addition, using footpoint brightenings at conjugate flare ribbons, a weakening reconnection guide field is inferred, which may also contribute to the hardening of the non-thermal electrons during the post-impulsive phase.

astro-ph.SR↗

arXiv2024

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1-minute and 5-minute resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1-minute and 5-minute resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hour in advance) in a large storm (SYM-H = -393 nT) using 5-minute resolution data. When predicting the SYM-H indices (2 hours in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

astro-ph.IM↗

arXiv2024

High Resolution Imaging Spectroscopy of a Tiny Sigmoidal Mini-filament Eruption

Minifilament (MF) eruption producing small jets and micro-flares is regarded as an important source for coronal heating and the solar wind transients through studies mostly based on coronal observations in the extreme ultraviolet (EUV) and X-ray wavelengths. In this study, we focus on the chromospheric plasma diagnostics of a tiny minifilament in quiet Sun located at [71'', 450''] on 2021--08--07 at 19:11 UT observed as part of the ninth encounter of the PSP campaign. Main data obtained are the high cadence, high resolution spectroscopy from the Fast Imaging Solar Spectrograph (FISS) and high-resolution magnetograms from the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the 1.6~m Goode Solar Telescope (GST) at Big Bear Solar Observatory (BBSO). The mini-filament with size $\sim$1''$\times$5'' and a micro-flare are detected in both the H$α$ line center and SDO/AIA 193, 304~Å images. On the NIRIS magnetogram, we found that the cancellation of a magnetic bipole in the footpoints of the minifilament triggered its eruption in a sigmoidal shape. By inversion of the \ha\ and Ca {\sc ii} spectra under the embedded cloud model, we found a temperature increase of 3,800 K in the brightening region, associated with rising speed average of MF increased by 18~$km~s^{-1}$. This cool plasma is also found in the EUV images. We estimate the kinetic energy change of the rising filament as 1.5$\times$$10^{25}$~ergs, and thermal energy accumulation in the MF, 1.4$\times$$10^{25}$~ergs. From the photospheric magnetograms, we find the magnetic energy change is 1.6$\times$$10^{26}$~ergs across the PIL of converging opposite magnetic elements, which amounts to the energy release in the chromosphere in this smallest two-ribbon flare ever observed.

astro-ph.SR↗

arXiv2023

Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models

Coronal mass ejections (CMEs) are massive solar eruptions, which have a significant impact on Earth. In this paper, we propose a new method, called DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic energy. Being able to estimate these properties helps better understand CME dynamics. Our study is based on the CME catalog maintained at the Coordinated Data Analysis Workshops (CDAW) Data Center, which contains all CMEs manually identified since 1996 using the Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO). We use LASCO C2 data in the period between January 1996 and December 2020 to train, validate and test DeepCME through 10-fold cross validation. The DeepCME method is a fusion of three deep learning models, including ResNet, InceptionNet, and InceptionResNet. Our fusion model extracts features from LASCO C2 images, effectively combining the learning capabilities of the three component models to jointly estimate the mass and kinetic energy of CMEs. Experimental results show that the fusion model yields a mean relative error (MRE) of 0.013 (0.009, respectively) compared to the MRE of 0.019 (0.017, respectively) of the best component model InceptionResNet (InceptionNet, respectively) in estimating the CME mass (kinetic energy, respectively). To our knowledge, this is the first time that deep learning has been used for CME mass and kinetic energy estimations.

astro-ph.SR↗

arXiv2023

A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 AU

Interplanetary magnetic flux ropes (MFRs) are commonly observed structures in the solar wind, categorized as magnetic clouds (MCs) and small-scale MFRs (SMFRs) depending on whether they are associated with coronal mass ejections. We apply machine learning to systematically compare SMFRs, MCs, and ambient solar wind plasma properties. We construct a dataset of 3-minute averaged sequential data points of the solar wind's instantaneous bulk fluid plasma properties using about twenty years of measurements from \emph{Wind}. We label samples by the presence and type of MFRs containing them using a catalog based on Grad-Shafranov (GS) automated detection for SMFRs and NASA's catalog for MCs (with samples in neither labeled non-MFRs). We apply the random forest machine learning algorithm to find which categories can be more easily distinguished and by what features. MCs were distinguished from non-MFRs with an AUC of 94% and SMFRs with an AUC of 89% and had distinctive plasma properties. In contrast, while SMFRs were distinguished from non-MFRs with an AUC of 86%, this appears to rely solely on the $\langle B \rangle$ > 5 nT threshold applied by the GS catalog. The results indicate that SMFRs have virtually the same plasma properties as the ambient solar wind, unlike the distinct plasma regimes of MCs. We interpret our findings as additional evidence that most SMFRs at 1 au are generated within the solar wind, and furthermore, suggesting that they should be considered a salient feature of the solar wind's magnetic structure rather than transient events.

physics.space-ph↗

arXiv2023

A Closer Look at Small-Scale Magnetic Flux Ropes in the Solar Wind at 1 AU: Results from Improved Automated Detection

Small-scale interplanetary magnetic flux ropes (SMFRs) are similar to ICMEs in magnetic structure, but are smaller and do not exhibit ICME plasma signatures. We present a computationally efficient and GPU-powered version of the single-spacecraft automated SMFR detection algorithm based on the Grad-Shafranov (GS) technique. Our algorithm is capable of processing higher resolution data, eliminates selection bias caused by a fixed $\avg{B}$ threshold, has improved detection criteria demonstrated to have better results on an MHD simulation, and recovers full 2.5D cross sections using GS reconstruction. We used it to detect 512,152 SMFRs from 27 years (1996 to 2022) of 3-second cadence \emph{Wind} measurements. Our novel findings are: (1) the radial density of SMFRs at 1 au (${\sim}1$ per $\si{10^6\kilo\meter}$) and filling factor (${\sim}$35\%) are independent of solar activity, distance to the heliospheric current sheet (HCS), and solar wind plasma type, although the minority of SMFRs with diameters greater than ${\sim}$0.01 au have a strong solar activity dependence; (2) SMFR diameters follow a log-normal distribution that peaks below the resolved range ($\gtrsim 10^4$ km), although the filling factor is dominated by SMFRs between $10^5$ to $10^6$ km; (3) most SMFRs at 1 au have strong field-aligned flows like those from PSP measurements; (4) in terms of diameter $d$, SMFR poloidal flux $\propto d^{1.2}$, axial flux $\propto d^{2.0}$, average twist number $\propto d^{-0.8}$, current density $\propto d^{-0.8}$, and helicity $\propto d^{3.2}$. Implications for the origin of SMFRs and switchbacks are briefly discussed. The new algorithm and SMFR dataset are made freely available.

physics.space-ph↗

arXiv2023

Extreme Red-wing Enhancements of UV Lines During the 2022 March 30 X1.3 Solar Flare

Here we present the study of a compact emission source during an X1.3 flare on 2022-March-30. Within a $\sim41$~s period (17:34:48 UT to 17:35:29 UT), IRIS observations show spectral lines of Mg II, C II and Si IV with extremely broadened, asymmetric red-wings. This source of interest (SOI) is compact, $\sim$ 1\arcsec.6, and is located in the wake of a passing ribbon. Two methods were applied to measure the Doppler velocities associated with these red wings: spectral moments and multi-Gaussian fits. The spectral moments method considers the averaged shift of the lines, which are 85 km s$^{-1}$, 125 km s$^{-1}$ and 115 km s$^{-1}$ for the Mg II, C II and Si IV lines respectively. The red-most Gaussian fit suggests a Doppler velocity up to $\sim$160 km s$^{-1}$ in all of the three lines. Downward mass motions with such high speeds are very atypical, with most chromospheric downflows in flares on the order 10-100 km s$^{-1}$. Furthermore, EUV emission is strong within flaring loops connecting two flare ribbons located mainly to the east of the central flare region. The EUV loops that connect the SOI and its counterpart source in the opposite field are much less brightened, indicating that the density and/or temperature is comparatively low. These observations suggest a very fast downflowing plasma in transition region and upper chromosphere, that decelerates rapidly since there is no equivalently strong shift of the O I chromospheric lines. This unusual observation presents a challenge that models of the solar atmosphere's response to flares must be able to explain.

astro-ph.SR↗

arXiv2023

The Solar Origin of an In Situ Type III Radio Burst Event

Solar type III radio bursts are generated by beams of energetic electrons that travel along open magnetic field lines through the corona and into interplanetary space. However, understanding the source of these electrons and how they escape into interplanetary space remains an outstanding topic. Here we report multi-instrument, multi-perspective observations of an interplanetary type III radio burst event shortly after the second perihelion of the Parker Solar Probe (PSP). This event was associated with a solar jet that produced an impulsive microwave burst event recorded by the Expanded Owens Valley Solar Array (EOVSA). The type III burst event also coincided with the detection of enhanced in situ energetic electrons recorded by both PSP at 0.37 AU and WIND at 1 AU, which were located very closely on the Parker spiral longitudinally. The close timing association and magnetic connectivity suggest that the in situ energetic electrons originated from the jet's magnetic reconnection region. Intriguingly, microwave imaging spectroscopy results suggest that the escaping energetic electrons were injected into a large opening angle of about 90 degrees, which is at least nine times broader than the apparent width of the jet spire. Our findings provide an interpretation for the previously reported, longitudinally broad spatial distribution of flare locations associated with prompt energetic electron events and have important implications for understanding the origin and distribution of energetic electrons in the interplanetary space.

astro-ph.SR↗

arXiv2023

Coronal Magnetic Field Extrapolation and Topological Analysis of Fine-Scale Structures during Solar Flare Precursors

Magnetic field plays an important role in various solar eruptions like flares, coronal mass ejections, etc. The formation and evolution of characteristic magnetic field topology in solar eruptions are critical problems that will ultimately help us understand the origination of these eruptions in the solar source regions. With the development of advanced techniques and instruments, observations with higher resolutions in different wavelengths and fields of view have provided more quantitative information for finer structures. So it is essential to improve our method to study the magnetic field topology in the solar source regions by taking advantage of high-resolution observations. In this study, we employ a nonlinear force-free field (NLFFF) extrapolation method based on a nonuniform grid setting for an M-class flare eruption event (SOL2015-06-22T17:39) with embedded magnetograms from the Solar Dynamics Observatory (SDO) and the Goode Solar Telescope (GST). The extrapolation results employing the embedded magnetogram for the bottom boundary are obtained by maintaining the native resolutions of the corresponding GST and SDO magnetograms. We compare the field line connectivity with the simultaneous GST/H$α$ and SDO/AIA observations for fine-scale structures associated with precursor brightenings. Then we perform a topological analysis of the field line connectivity corresponding to fine-scale magnetic field structures based on the extrapolation results. The results indicate that by combining the high-resolution GST magnetogram with a larger HMI magnetogram, the derived magnetic field topology is consistent with a scenario of magnetic reconnection among sheared field lines across the main polarity inversion line during solar flare precursors.

astro-ph.SR↗

arXiv2023

Multi-instrument Comparative Study of Temperature, Number Density, and Emission Measure during the Precursor Phase of a Solar Flare

We present a multi-instrument study of the two precursor brightenings prior to the M6.5 flare (SOL2015-06-22T18:23) in the NOAA Active Region 12371, with a focus on the temperature (T), electron number density (n), and emission measure (EM). The data used in this study were obtained from four instruments with a variety of wavelengths, i.e., the Solar Dynamics Observatory's Atmospheric Imaging Assembly (AIA), in six extreme ultraviolet (EUV) passbands; the Expanded Owens Valley Solar Array (EOVSA) in microwave (MW); the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) in hard X-rays (HXR); and the Geostationary Operational Environmental Satellite (GOES) in soft X-rays (SXR). We compare the temporal variations of T, n, and EM derived from the different data sets. Here are the key results. (1) GOES SXR and AIA EUV have almost identical EM variations (1.5-3x10^48 per cm^3) and very similar T variations, from 8 to 15 million Kelvin (MK). (2) Listed from highest to lowest, EOVSA MW provides the highest temperature variations (15-60 MK), followed by RHESSI HXR (10-24 MK), then GOES SXR and AIA EUV (8-15 MK). (3) The EM variation from the RHESSI HXR measurements is always less than the values from AIA EUV and GOES SXR by at most 20 times. The number density variation from EOVSA MW is greater than the value from AIA EUV by at most 100 times. The results quantitatively describe the differences in the thermal parameters at the precursor phase, as measured by different instruments operating at different wavelength regimes and for different emission mechanisms.

astro-ph.SR↗

arXiv2023

A Comparative Study of Solar Active Region 12371 with Data-constrained and Data-driven MHD Simulations

We performed two data-based magnetohydrodynamic (MHD) simulations for solar active region 12371 which produced an M6.5 flare. The first simulation is a full data-driven simulation where the initial condition is given by a nonlinear force-free field (NLFFF). This NLFFF was extrapolated from photospheric magnetograms approximately 1 hour prior to the flare, and then a time-varying photospheric magnetic field is imposed at the bottom surface. The second simulation is also a data-driven simulation, but it stops driving at the bottom before the time of flare onset and then switches to the data-constrained simulation, where the horizontal component of the magnetic field varies according to an induction equation while the normal component is fixed with time. Both simulations lead to an eruption, with both simulations producing highly twisted field lines before the eruption which were not found in the NLFFF alone. After the eruption, the first simulation based on the time-varying photospheric magneitic field, continues to produce sheared field lines after the flare without reproducing phenomena such as post-flare loops. The second simulation reproduces the phenomena associated with flares well. However in this case the evolution of the bottom magnetic field is inconsistent with the evolution of the observed magnetic field. In this letter, we report potential advantages and disadvantages in data-constrained and data-driven MHD simulations that need to be taken into consideration by future studies.

astro-ph.SR↗

arXiv2022

Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods

Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections (CMEs). They are the most important sources of space weather effects, that can severely impact the near-Earth environment. Thus it is essential to forecast flares (especially the M-and X-class ones) to mitigate their destructive and hazardous consequences. Here, we introduce several statistical and Machine Learning approaches to the prediction of the AR's Flare Index (FI) that quantifies the flare productivity of an AR by taking into account the numbers of different class flares within a certain time interval. Specifically, our sample includes 563 ARs appeared on solar disk from May 2010 to Dec 2017. The 25 magnetic parameters, provided by the Space-weather HMI Active Region Patches (SHARP) from Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO), characterize coronal magnetic energy stored in ARs by proxy and are used as the predictors. We investigate the relationship between these SHARP parameters and the FI of ARs with a machine-learning algorithm (spline regression) and the resampling method (Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise, short by SMOGN). Based on the established relationship, we are able to predict the value of FIs for a given AR within the next 1-day period. Compared with other 4 popular machine learning algorithms, our methods improve the accuracy of FI prediction, especially for large FI. In addition, we sort the importance of SHARP parameters by Borda Count method calculated from the ranks that are rendered by 9 different machine learning methods.

astro-ph.SR↗

arXiv2022

A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data

Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have been used to analyze and forecast eruptive events such as solar flares and coronal mass ejections. Unfortunately, the most recent solar cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) since its launch in 2010. In this paper, we look into another major instrument, namely the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers more active solar cycle 23 with many large flares. However, SOHO/MDI data only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the proposed method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of solar active regions for SOHO/MDI using SDO/HMI and H-alpha data.

astro-ph.SR↗

arXiv2022

A Data-constrained Magnetohydrodynamic Simulation of the X1.0 Solar Flare of 2021 October 28

The solar active region NOAA 12887 produced a strong X1.0 flare on 2021 October 28, which exhibits X-shaped flare ribbons and a circle-shaped erupting filament. To understand the eruption process with these characteristics, we conducted a data-constrained magnetohydrodynamics simulation using a nonlinear force-free field of the active region about an hour before the flare as the initial condition. Our simulation reproduces the filament eruption observed in the Ha images of GONG and the 304 angstrom images of SDO/AIA and suggests that two mechanisms can possibly contribute to the magnetic eruption. One is the torus instability of the pre-existing magnetic flux rope (MFR), and the other is upward pushing by magnetic loops newly formed below the MFR via continuous magnetic reconnection between two sheared magnetic arcades. The presence of this reconnection is evidenced by the SDO/AIA observations of the 1600 angstrom brightening in the footpoints of the sheared arcades at the flare onset. To clarify which process is more essential for the eruption, we performed an experimental simulation in which the reconnection between the sheared field lines is suppressed. In this case too, the MFR could erupt, but at a much reduced rising speed. We interpret this result as indicating that the eruption is not only driven by the torus instability, but additionally accelerated by newly formed and rising magnetic loops under continuous reconnection.

astro-ph.SR↗

arXiv2022

Inferring Line-of-Sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks

Obtaining high-quality magnetic and velocity fields through Stokes inversion is crucial in solar physics. In this paper, we present a new deep learning method, named Stacked Deep Neural Networks (SDNN), for inferring line-of-sight (LOS) velocities and Doppler widths from Stokes profiles collected by the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the 1.6 m Goode Solar Telescope (GST) at the Big Bear Solar Observatory (BBSO). The training data of SDNN is prepared by a Milne-Eddington (ME) inversion code used by BBSO. We quantitatively assess SDNN, comparing its inversion results with those obtained by the ME inversion code and related machine learning (ML) algorithms such as multiple support vector regression, multilayer perceptrons and a pixel-level convolutional neural network. Major findings from our experimental study are summarized as follows. First, the SDNN-inferred LOS velocities are highly correlated to the ME-calculated ones with the Pearson product-moment correlation coefficient being close to 0.9 on average. Second, SDNN is faster, while producing smoother and cleaner LOS velocity and Doppler width maps, than the ME inversion code. Third, the maps produced by SDNN are closer to ME's maps than those from the related ML algorithms, demonstrating the better learning capability of SDNN than the ML algorithms. Finally, comparison between the inversion results of ME and SDNN based on GST/NIRIS and those from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory in flare-prolific active region NOAA 12673 is presented. We also discuss extensions of SDNN for inferring vector magnetic fields with empirical evaluation.

astro-ph.SR↗

arXiv2022

2022 Review of Data-Driven Plasma Science

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.

physics.plasm-ph↗