arXiv subjects

Barbara J. Thompson

Publications and source records attributed to Barbara J. Thompson.

16 recordsLinked to original sources

arXiv2025

Spectral and Spatial Graph Learning for Multispectral Solar Image Compression

High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spatial details. We present a learned image compression framework tailored to solar observations, leveraging two complementary modules: (1) the Inter-Spectral Windowed Graph Embedding (iSWGE), which explicitly models inter-band relationships by representing spectral channels as graph nodes with learned edge features; and (2) the Windowed Spatial Graph Attention and Convolutional Block Attention (WSGA-C), which combines sparse graph attention with convolutional attention to reduce spatial redundancy and emphasize fine-scale structures. Evaluations on the SDOML dataset across six extreme ultraviolet (EUV) channels show that our approach achieves a 20.15%reduction in Mean Spectral Information Divergence (MSID), up to 1.09% PSNR improvement, and a 1.62% log transformed MS-SSIM gain over strong learned baselines, delivering sharper and spectrally faithful reconstructions at comparable bits-per-pixel rates. The code is publicly available at https://github.com/agyat4/sgraph .

cs.CV↗

arXiv2025

von Karman - Howarth Similarity of Spatial Correlations and the Distribution of Correlation Lengths in Solar Photospheric Turbulence

Fluctuations in the Sun's photospheric magnetic field are the primary source of the turbulence that can heat and accelerate the solar atmosphere, and thus play an important role in the production and evolution of the solar wind that permeates the heliosphere. A key parameter that characterizes this turbulence is the correlation scale of fluctuations, which determines the injection of turbulent energy into the plasma and the diffusive transport of solar energetic particles. This study employs magnetogram data from the Helioseismic and Magnetic Imager on the Solar Dynamics Observatory to characterize an ensemble of spatial autocorrelation functions (ACFs) of turbulence in the photosphere. It is shown that the two-point ACFs satisfy the similarity-decay hypothesis of von K\'arm\'an and Howarth, a fundamental property of turbulent systems: rescaling the ACFs by their respective energies and correlation lengths yields a quasi-universal exponential form. The probability distribution function of transverse correlation lengths ($\lambda$) is shown to be approximately log-normal, which is consistent with observations of turbulence in the solar wind. A ``mosaic'' of the spatial distribution of $\lambda$ over the photosphere is presented; the ``quiet Sun'' tends to have $\lambda\sim 1500$ km (albeit with a wide distribution), which is close to the scale of solar granulation; systematically longer lengths are associated with active regions. A positive correlation is observed between mean magnetic field magnitude and $\lambda$, and empirical fits quantify this relationship. These results improve our understanding of solar turbulence while providing observational constraints for models that describe turbulence transport from solar and stellar photospheres into their atmospheres.

astro-ph.SR↗

arXiv2025

Polarimeter to Unify the Corona and Heliosphere (PUNCH)

The Polarimeter to Unify the Corona and Heliosphere (PUNCH) mission is a NASA Small Explorer to determine the cross-scale processes that unify the solar corona and heliosphere. PUNCH has two science objectives: (1) understand how coronal structures become the ambient solar wind, and (2) understand the dynamic evolution of transient structures, such as coronal mass ejections, in the young solar wind. To address these objectives, PUNCH uses a constellation of four small spacecraft in Sun-synchronous low Earth orbit, to collect linearly polarized images of the K corona and young solar wind. The four spacecraft each carry one visible-light imager in a 1+3 configuration: a single Narrow Field Imager solar coronagraph captures images of the outer corona at all position angles, and at solar elongations from 1.5 degrees (6 R$_\odot$) to 8 degrees (32 R$_\odot$); and three separate Wide Field Imager heliospheric imagers together capture views of the entire inner solar system, at solar elongations from 3 degrees (12 R$_\odot$) to 45 degrees (180 R$_\odot$) from the Sun. PUNCH images include linear-polarization data, to enable inferring the three-dimensional structure of visible features without stereoscopy. The instruments are matched in wavelength passband, support overlapping instantaneous fields of view, and are operated synchronously, to act as a single ``virtual instrument'' with a 90 degree wide field of view, centered on the Sun. PUNCH launched in March of 2025 and began science operations in June of 2025. PUNCH has an open data policy with no proprietary period, and PUNCH Science Team Meetings are open to all.

astro-ph.SR↗

arXiv2024

Neural-based Video Compression on Solar Dynamics Observatory Images

NASA's Solar Dynamics Observatory (SDO) mission collects extensive data to monitor the Sun's daily activity. In the realm of space mission design, data compression plays a crucial role in addressing the challenges posed by limited telemetry rates. The primary objective of data compression is to facilitate efficient data management and transmission to work within the constrained bandwidth, thereby ensuring that essential information is captured while optimizing the utilization of available resources. This paper introduces a neural video compression technique that achieves a high compression ratio for the SDO's image data collection. The proposed approach focuses on leveraging both temporal and spatial redundancies in the data, leading to a more efficient compression. In this work, we introduce an architecture based on the Transformer model, which is specifically designed to capture both local and global information from input images in an effective and efficient manner. Additionally, our network is equipped with an entropy model that can accurately model the probability distribution of the latent representations and improves the speed of the entropy decoding step. The entropy model leverages a channel-dependent approach and utilizes checkerboard-shaped local and global spatial contexts. By combining the Transformer-based video compression network with our entropy model, the proposed compression algorithm demonstrates superior performance over traditional video codecs like H.264 and H.265, as confirmed by our experimental results.

eess.IV↗

arXiv2024

SynCOM: An Empirical Model for High-Resolution Simulations of Transient Solar Wind Flows

The Synthetic Corona Outflow Model (SynCOM), an empirical model, simulates the solar corona's dynamics to match high-resolution observations, providing a useful resource for testing velocity measurement algorithms. SynCOM generates synthetic images depicting radial variability in polarized brightness and includes stochastic elements for plasma outflows and instrumental noise. It employs a predefined flow velocity probability distribution and an adjustable signal-to-noise ratio to evaluate different data analysis methods for coronal flows. By adjusting parameters to match specific coronal and instrumental conditions, SynCOM offers a platform to assess these methods for determining coronal velocity and acceleration. Validating these measurements would help to understand solar wind origins and support missions such as the Polarimeter to Unify the Corona and Heliosphere (PUNCH). In this study, we demonstrate how SynCOM can be employed to assess the precision and performance of two different flow tracking methods. By providing a ground-truth based on observational data, we highlight the importance of SynCOM in confirming observational standards for detecting coronal flows.

astro-ph.SR↗

arXiv2023

Neural-based Compression Scheme for Solar Image Data

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.

eess.IV↗

arXiv2023

Multi-spectral Entropy Constrained Neural Compression of Solar Imagery

Missions studying the dynamic behaviour of the Sun are defined to capture multi-spectral images of the sun and transmit them to the ground station in a daily basis. To make transmission efficient and feasible, image compression systems need to be exploited. Recently successful end-to-end optimized neural network-based image compression systems have shown great potential to be used in an ad-hoc manner. In this work we have proposed a transformer-based multi-spectral neural image compressor to efficiently capture redundancies both intra/inter-wavelength. To unleash the locality of window-based self attention mechanism, we propose an inter-window aggregated token multi head self attention. Additionally to make the neural compressor autoencoder shift invariant, a randomly shifted window attention mechanism is used which makes the transformer blocks insensitive to translations in their input domain. We demonstrate that the proposed approach not only outperforms the conventional compression algorithms but also it is able to better decorrelates images along the multiple wavelengths compared to single spectral compression.

eess.IV↗

arXiv2023

Context-Aware Neural Video Compression on Solar Dynamics Observatory

NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity. Data compression is crucial for space missions to reduce data storage and video bandwidth requirements by eliminating redundancies in the data. In this paper, we present a novel neural Transformer-based video compression approach specifically designed for the SDO images. Our primary objective is to efficiently exploit the temporal and spatial redundancies inherent in solar images to obtain a high compression ratio. Our proposed architecture benefits from a novel Transformer block called Fused Local-aware Window (FLaWin), which incorporates window-based self-attention modules and an efficient fused local-aware feed-forward (FLaFF) network. This architectural design allows us to simultaneously capture short-range and long-range information while facilitating the extraction of rich and diverse contextual representations. Moreover, this design choice results in reduced computational complexity. Experimental results demonstrate the significant contribution of the FLaWin Transformer block to the compression performance, outperforming conventional hand-engineered video codecs such as H.264 and H.265 in terms of rate-distortion trade-off.

eess.IV↗

arXiv2023

The Plasma-prescribed Active Region Static Extrapolation (PARSE) Dataset: A Machine-Learning-Ready Collection of Magnetohydrostatic Coronal Active Regions

As Physics-Informed Neural Networks and other methods for full-vector-field construction or analysis become more prominent, a need has developed for a large set of simulated active regions for training, validation and testing purposes. We use a state-of-the-art magnetohydrostatic extrapolation method to develop a public dataset of over five thousand data cubes based on the Spaceweather HMI Active Region Patch (SHARP) library of active region magnetogram images. Each cube resolves the magnetic field vector and plasma forcing at approximately 100,000 scattered points that are adaptively clustered near the high-flux regions of the domain. This paper describes the methodology of construction of the Plasma-prescribed Active Region Static Extrapolation (PARSE) dataset, as well as its structure and how to access it.

astro-ph.SR↗

arXiv2023

Attention-Based Generative Neural Image Compression on Solar Dynamics Observatory

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 trade-off 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.

eess.IV↗

arXiv2023

Defining the Middle Corona

The middle corona, the region roughly spanning heliocentric altitudes from $1.5$ to $6\,R_\odot$, encompasses almost all of the influential physical transitions and processes that govern the behavior of coronal outflow into the heliosphere. Eruptions that could disrupt the near-Earth environment propagate through it. Importantly, it modulates inflow from above that can drive dynamic changes at lower heights in the inner corona. Consequently, this region is essential for comprehensively connecting the corona to the heliosphere and for developing corresponding global models. Nonetheless, because it is challenging to observe, the middle corona has been poorly studied by major solar remote sensing missions and instruments, extending back to the Solar and Heliospheric Observatory (SoHO) era. Thanks to recent advances in instrumentation, observational processing techniques, and a realization of the importance of the region, interest in the middle corona has increased. Although the region cannot be intrinsically separated from other regions of the solar atmosphere, there has emerged a need to define the region in terms of its location and extension in the solar atmosphere, its composition, the physical transitions it covers, and the underlying physics believed to be encapsulated by the region. This paper aims to define the middle corona and give an overview of the processes that occur there.

astro-ph.SR↗

arXiv2021

SunCET: A compact EUV instrument to fill a critical observational gap

The Sun Coronal Ejection Tracker (SunCET) is an extreme ultraviolet imager and spectrograph instrument concept for tracking coronal mass ejections through the region where they experience the majority of their acceleration: the difficult-to-observe middle corona. It contains a wide field of view (0-4~\Rs) imager and a 1~Å spectral-resolution-irradiance spectrograph spanning 170-340~Å. It leverages new detector technology to read out different areas of the detector with different integration times, resulting in what we call "simultaneous high dynamic range", as opposed to the traditional high dynamic range camera technique of subsequent full-frame images that are then combined in post-processing. This allows us to image the bright solar disk with short integration time, the middle corona with a long integration time, and the spectra with their own, independent integration time. Thus, SunCET does not require the use of an opaque or filtered occulter. SunCET is also compact -- $\sim$15 $\times$ 15 $\times$ 10~cm in volume -- making it an ideal instrument for a CubeSat or a small, complementary addition to a larger mission. Indeed, SunCET is presently in a NASA-funded, competitive Phase A as a CubeSat and has also been proposed to NASA as an instrument onboard a 184 kg Mission of Opportunity.

astro-ph.SR↗

arXiv2019

AWARE: An algorithm for the automated characterization of EUV waves in the solar atmosphere

Extreme ultraviolet (EUV) waves are large-scale propagating disturbances observed in the solar corona, frequently associated with coronal mass ejections and flares. They appear as faint, extended structures propagating from a source region across the structured solar corona. Since their discovery, over two hundred papers discussing their properties, causes and physical nature have been published. However, despite this their fundamental properties and the physics of their interactions with other solar phenomena are still not understood. To further the understanding of EUV waves, we have constructed the Automated Wave Analysis and REduction (AWARE) algorithm for the measurement of EUV waves. AWARE is implemented in two stages. In the first stage, we use a new type of running difference image, the running difference persistence image, which enables the efficient isolation of propagating, brightening wavefronts as they propagate across the corona. In the second stage, AWARE detects the presence of a wavefront, and measures the distance, velocity and acceleration of that wavefront across the Sun. The fit of propagation models to the wave progress isolated in the first stage is achieved using the Random Sample and Consensus (RANSAC) algorithm. AWARE is tested against simulations of EUV wave propagation, and is applied to measure EUV waves in observational data from the Atmospheric Imaging Assembly (AIA). We also comment on unavoidable systematic errors that bias the estimation of wavefront velocity and acceleration. In addition, the full AWARE software suite comes with a package that creates simulations of waves propagating across the disk from arbitrary starting points.

astro-ph.SR↗

arXiv2019

Application Usability Levels: A Framework for Tracking Project Product Progress

The space physics community continues to grow and become both more interdisciplinary and more intertwined with commercial and government operations. This has created a need for a framework to easily identify what projects can be used for specific applications and how close the tool is to routine autonomous or on-demand implementation and operation. We propose the Application Usability Level (AUL) framework and publicizing AULs to help the community quantify the progress of successful applications, metrics, and validation efforts. This framework will also aid the scientific community by supplying the type of information needed to build off of previously published work and publicizing the applications and requirements needed by the user communities. In this paper, we define the AUL framework, outline the milestones required for progression to higher AULs, and provide example projects utilizing the AUL framework. This work has been completed as part of the activities of the Assessment of Understanding and Quantifying Progress working group which is part of the International Forum for Space Weather Capabilities Assessment.

astro-ph.IM↗

arXiv2016

Relationship of EUV Irradiance Coronal Dimming Slope and Depth to Coronal Mass Ejection Speed and Mass

Extreme ultraviolet (EUV) coronal dimmings are often observed in response to solar eruptive events. These phenomena can be generated via several different physical processes. For space weather, the most important of these is the temporary void left behind by a coronal mass ejection (CME). Massive, fast CMEs tend to leave behind a darker void that also usually corresponds to minimum irradiance for the cooler coronal emissions. If the dimming is associated with a solar flare, as is often the case, the flare component of the irradiance light curve in the cooler coronal emission can be isolated and removed using simultaneous measurements of warmer coronal lines. We apply this technique to 37 dimming events identified during two separate two-week periods in 2011, plus an event on 2010 August 7 analyzed in a previous paper, to parameterize dimming in terms of depth and slope. We provide statistics on which combination of wavelengths worked best for the flare-removal method, describe the fitting methods applied to the dimming light curves, and compare the dimming parameters with corresponding CME parameters of mass and speed. The best linear relationships found are $$ \begin{align} v_{CME} \Big[\frac{km}{s} \Big] & \approx 2.36 \times 10^6 \Big[\frac{km}{\%} \Big] \times s_{dim} \Big[\frac{\%}{s} \Big] \\ m_{CME} [g] & \approx 2.59 \times 10^{15} \Big[\frac{g}{\%} \Big] \times \sqrt{d_{dim}} [\%]. \end{align} $$ These relationships could be used for space weather operations of estimating CME mass and speed using near-realtime irradiance dimming measurements.

astro-ph.SR↗

arXiv2011

SDO/AIA Observation of Kelvin-Helmholtz Instability in the Solar Corona

We present observations of the formation, propagation and decay of vortex- shaped features in coronal images from the Solar Dynamics Observatory (SDO) associated with an eruption starting at about 2:30UT on Apr 8, 2010. The series of vortices formed along the interface between an erupting (dimming) region and the surrounding corona. They ranged in size from several to ten arcseconds, and traveled along the interface at 6-14 km/s. The features were clearly visible in six out of the seven different EUV wavebands of the Atmospheric Imaging Assembly (AIA). Based on the structure, formation, propagation and decay of these features, we identified these features as the first observations of the Kelvin-Helmholtz (KH) instability in the corona in EUV. The interpretation is supported by linear analysis and by MHD model of KH instability. We conclude that the instability is driven by the velocity shear between the erupting and closed magnetic field of the Coronal Mass Ejection (CME). The shear flow driven instability can play an important role in energy transfer processes in coronal plasma.

astro-ph.SR↗