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Coronal holes (CH) are magnetically open regions that allow hot coronal plasma to escape from the Sun and form the high-speed solar wind. This wind can interact with Earth's magnetic field. For this reason, developing an accurate understanding of CH regions is vital for understanding space weather and its effects on Earth. The process of identifying CH regions typically relies on extreme ultraviolet (EUV) imagery, leveraging the fact that CHs appear dark at these wavelengths. Accurate identification of CHs in EUV, however, can be difficult due to a variety of factors, including stray light from nearby regions, limb brightening, and the presence of filaments (which also appear dark, but are not sources of solar wind). In order to overcome these issues, this work incorporates photospheric magnetic field data into a classical EUV-based segmentation algorithm based on the active contours without edges (ACWE) segmentation method. In this work magnetic field data are incorporated directly into the segmentation process, serving both as a method for removing non-CH regions in advance, and as a method to constrain evolution of the boundary which identifies the CH boundary. This reduces the presence of filaments while simultaneously allowing the segmentation to include CH regions that may be difficult to identify due to the aforementioned inconsistent intensities.

Coronal Holes (CHs) are large-scale, low-density regions in the solar atmosphere which may expel high-speed solar wind streams that incite hazardous, geomagnetic storms. Coronal and solar wind models can predict these high-speed streams and the performance of the coronal model can be validated against segmented CH boundaries. We present a novel method named Sub-Transition Region Identification of Ensemble Coronal Holes (STRIDE-CH) to address prominent challenges in segmenting CHs with Extreme Ultraviolet (EUV) imagery. Ground-based, chromospheric He I 10830 {\AA} line imagery and underlying Fe I photospheric magnetograms are revisited to disambiguate CHs from filaments and quiet Sun, overcome obscuration by coronal loops, and complement established methods in the community which use space-borne, coronal EUV observations. Classical computer vision techniques are applied to constrain the radiative and magnetic properties of detected CHs, produce an ensemble of boundaries, and compile these boundaries in a confidence map that quantifies the likelihood of CH presence throughout the solar disk. This method is science-enabling towards future studies of CH formation and variability from a mid-atmospheric perspective., Comment: 23 pages, 7 figures

The non-uniform blur of atmospheric turbulence can be modeled as a superposition of linear motion blur kernels at a patch level. We propose a regression convolutional neural network (CNN) to predict angle and length of a linear motion blur kernel for varying sized patches. We analyze the robustness of the network for different patch sizes and the performance of the network in regions where the characteristics of the blur are transitioning. Alternating patch sizes per epoch in training, we find coefficient of determination scores across a range of patch sizes of $R^2>0.78$ for length and $R^2>0.94$ for angle prediction. We find that blur predictions in regions overlapping two blur characteristics transition between the two characteristics as overlap changes. These results validate the use of such a network for prediction of non-uniform blur characteristics at a patch level.

Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make. Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy which could affect scientific analysis of the data. On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression. In this work, we propose a neural network-based lossy compression method to be used in NASA's data-intensive imagery missions. We chose NASA's SDO mission which transmits 1.4 terabytes of data each day as a proof of concept for the proposed algorithm. In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs. The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective. Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs such as JPEG and JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet (EUV) data. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images, and generated consistent segmentations, even at a compression rate of $\sim0.1$ bits per pixel (compared to 8 bits per pixel on the original data) using EUV data from SDO., Comment: Accepted for publication in IEEE Transactions on Aerospace and Electronic Systems (TAES). arXiv admin note: text overlap with arXiv:2210.06478

Coronal Holes (CHs) are regions of open magnetic field lines, resulting in high speed solar wind. Accurate detection of CHs is vital for space weather prediction. This paper presents an intramethod ensemble for coronal hole detection based on the Active Contours Without Edges (ACWE) segmentation algorithm. The purpose of this ensemble is to develop a confidence map that defines, for all on disk regions of a Solar extreme ultraviolet (EUV) image, the likelihood that each region belongs to a CH based on that region's proximity to, and homogeneity with, the core of identified CH regions. By relying on region homogeneity, and not intensity (which can vary due to various factors including line of sight changes and stray light from nearby bright regions), to define the final confidence of any given region, this ensemble is able to provide robust, consistent delineations of the CH regions. Using the metrics of global consistency error (GCE), local consistency error (LCE), intersection over union (IOU), and the structural similarity index measure (SSIM), the method is shown to be robust to different spatial resolutions maintaining a median IOU $>0.75$ and minimum SSIM $>0.93$ even when the segmentation process was performed on an EUV image decimated from $4096\times4096$ pixels down to $512\times512$ pixels. Furthermore, using the same metrics, the method is shown to be robust across short timescales, producing segmentation with a mean IOU of 0.826 from EUV images taken at a 1 hour cadence, and showing a smooth decay in similarity across all metrics as a function of time, indicating self-consistent segmentations even when corrections for exposure time have not been applied to the data. Finally, the accuracy of the segmentations and confidence maps are validated by considering the skewness (i.e., unipolarity) of the underlying magnetic field.

Many deblurring and blur kernel estimation methods use a maximum a posteriori (MAP) approach or deep learning-based classification techniques to sharpen an image and/or predict the blur kernel. We propose a regression approach using convolutional neural networks (CNNs) to predict parameters of linear motion blur kernels, the length and orientation of the blur. We analyze the relationship between length and angle of linear motion blur that can be represented as digital filter kernels. A large dataset of blurred images is generated using a suite of blur kernels and used to train a regression CNN for prediction of length and angle of the motion blur. The coefficients of determination for estimation of length and angle are found to be greater than or equal to 0.89, even under the presence of significant additive Gaussian noise, up to a variance of 10\% (SNR of 10 dB). Using our estimated kernel in a non-blind image deblurring method, the sum of squared differences error ratio demonstrates higher cumulative histogram values than comparison methods, with most test images yielding an error ratio of less than or equal to 1.25.

In this dataset we provide a comprehensive collection of magnetograms (images quantifying the strength of the magnetic field) from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions (regions of large magnetic flux, generally the source of eruptive events) as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset is a minimally processed, user configurable dataset of consistently sized images of solar active regions that can serve as a benchmark dataset for solar flare prediction research.

Recovering the turbulence-degraded point spread function from a single intensity image is important for a variety of imaging applications. Here, a deep learning model based on a convolutional neural network is applied to intensity images to predict a modified set of Zernike polynomial coefficients corresponding to wavefront aberrations in the pupil due to turbulence. The modified set assigns an absolute value to coefficients of even radial orders due to a sign ambiguity associated with this problem and is shown to be sufficient for specifying the intensity point spread function. Simulated image data of a point object and simple extended objects over a range of turbulence and detection noise levels are created for the learning model. The MSE results for the learning model show that the best prediction is found when observing a point object, but it is possible to recover a useful set of modified Zernike coefficients from an extended object image that is subject to detection noise and turbulence., Comment: 14 pages, 8 figures; title changed, typos corrected, references added

The Virtual Telescope for X-ray Observations (VTXO) will use lightweight Phase Fresnel Lenses (PFLs) in a virtual X-ray telescope with $\sim$1 km focal length and with $\sim$50 milli-arcsecond angular resolution. VTXO is formed by using precision formation flying of two SmallSats: a smaller OpticsSat that houses the PFLs and navigation beacons while a larger DetectorSat contains an X-ray camera, a precision start tracker, and the propulsion for the formation flying. The baseline flight dynamics uses a highly elliptical supersynchronous orbit allow the formation to hold in an inertial frame around the 90,000 km apogee for 10 hours of the 32.5 hour orbit with nearly a year mission lifetime. VTXO's fine angular resolution enables measuring the environments close to the central engines of bright compact X-ray sources. This X-ray imaging capability allows for the study of the effects of dust scattering near to the central objects such as Cyg X-3 and GX 5-1, for the search for jet structure near to the compact object in X-ray novae such as Cyg X-1 and GRS 1915+105, and for the search for structure in the termination shock of in the Crab pulsar wind nebula. The VTXO SmallSat and instrument designs, mission parameters, and science performance are described. VTXO development was supported as one of the selected 2018 NASA Astrophysics SmallSat Study (AS$^3$) missions., Comment: 6 pages, 7 figures, corrected version, Proceedings Volume 11444, Space Telescopes and Instrumentation 2020: Ultraviolet to Gamma Ray; 114447V (2020). arXiv admin note: text overlap with arXiv:2006.12174

This Innovative Practice Category Work In Progress paper presents an application of machine learning and data mining to student performance data in an undergraduate electrical engineering program. We are developing an analytical approach to enhance retention in the program especially among underrepresented groups. Our approach will provide quantitative assessment of student performance in courses. Specifically, by hierarchically mapping the content of assignments to course learning objectives, we can better decipher which concepts a particular student is struggling with and, with the help of peer mentors, create tailored intervention techniques to help the student be successful in the program. These results will also be useful to academic advisors who can work with the student to determine class schedules that promote success in the program. In addition, students can take a proactive approach to their learning. In our approach, data from our learning management system and other available sources will be used to predict several outcomes for individuals such as when a student is beginning to have trouble with the material or if factors outside of the classroom are affecting their success. Here, we present our initial database schema and preliminary results relating number of class re-takes to time-to-graduation. [This paper was published in: "2018 IEEE Frontiers in Education Conference (FIE)," IEEE, 2018, pp. 1-5.]

We study the prediction of solar flare size and time-to-flare using 38 features describing magnetic complexity of the photospheric magnetic field. This work uses support vector regression to formulate a mapping from the 38-dimensional feature space to a continuous-valued label vector representing flare size or time-to-flare. When we consider flaring regions only, we find an average error in estimating flare size of approximately half a \emph{geostationary operational environmental satellite} (\emph{GOES}) class. When we additionally consider non-flaring regions, we find an increased average error of approximately 3/4 a \emph{GOES} class. We also consider thresholding the regressed flare size for the experiment containing both flaring and non-flaring regions and find a true positive rate of 0.69 and a true negative rate of 0.86 for flare prediction. The results for both of these size regression experiments are consistent across a wide range of predictive time windows, indicating that the magnetic complexity features may be persistent in appearance long before flare activity. This is supported by our larger error rates of some 40 hr in the time-to-flare regression problem. The 38 magnetic complexity features considered here appear to have discriminative potential for flare size, but their persistence in time makes them less discriminative for the time-to-flare problem., Comment: http://iopscience.iop.org/article/10.1088/0004-637X/812/1/51/meta

We study the photospheric magnetic field of ~2000 active regions in solar cycle 23 to search for parameters indicative of energy build-up and subsequent release as a solar flare. We extract three sets of parameters: snapshots in space and time- total flux, magnetic gradients, and neutral lines; evolution in time- flux evolution; structures at multiple size scales- wavelet analysis. This combines pattern recognition and classification techniques via a relevance vector machine to determine whether a region will flare. We consider classification performance using all 38 extracted features and several feature subsets. Classification performance is quantified using both the true positive rate and the true negative rate. Additionally, we compute the true skill score which provides an equal weighting to true positive rate and true negative rate and the Heidke skill score to allow comparison to other flare forecasting work. We obtain a true skill score of ~0.5 for any predictive time window in the range 2-24hr, with a TPR of ~0.8 and a TNR of ~0.7. These values do not appear to depend on the time window, although the Heidke skill score (<0.5) does. Features relating to snapshots of the distribution of magnetic gradients show the best predictive ability over all predictive time windows. Other gradient-related features and the instantaneous power at various wavelet scales also feature in the top five ranked features in predictive power. While the photospheric magnetic field governs the coronal non-potentiality (and likelihood of flaring), photospheric magnetic field alone is not sufficient to determine this uniquely. Furthermore we are only measuring proxies of the magnetic energy build up. We still lack observational details on why energy is released at any particular point in time. We may have discovered the natural limit of the accuracy of flare predictions from these large scale studies.