arXiv subjects

Graham Barnes

Publications and source records attributed to Graham Barnes.

At least 19 recordsLinked to original sources

arXiv2026

Magnetic flux systems involved in the May 2024 solar energetic events from AR 13664 inferred through coronal dimmings

Coronal dimmings are transient depletions of coronal plasma observed in extreme ultraviolet and soft X-rays, interpreted as low-corona signatures of coronal mass ejections (CMEs). Their evolution is closely linked to CME dynamics, flare reconnection, and large-scale coronal magnetic restructuring. In May 2024, active region (AR) 13664 produced 66 > M-class flares and multiple fast CMEs that caused the strongest geomagnetic storm since 2003. We analysed 16 on-disc dimmings observed in SDO/AIA 211\AA data between May 1-14, 2024. We extracted dimmings using logarithmic base-ratio thresholding, and derived magnetic properties from SDO/HMI line-of-sight magnetograms. We identified flare ribbons in AIA 1600\AA data via adaptive thresholding, and computed reconnection fluxes from radial magnetic field maps. We examined the magnetic flux systems involved in the eruptions using PFSS and NLFF extrapolations. We found correlations between dimming and flare ribbon properties stronger than previously reported. The dimming morphology evolved systematically, with southward expansion before May 9 and northward afterward, coinciding with a shift in flare ribbon locations between two mayor east-west polarity inversion lines (PILs). These changes imply the presence of two distinct magnetic domains. The PFSS extrapolations showed that southward (northward) dimmings are mainly strapping flux dimmings with magnetic field lines vaulting above the southern (northern) PIL. The final extent of the dimmings was then given by the exterior flux involved in the eruption via stretching and reconnection. One event revealed an extended quiet-Sun dimming likely triggered by EUV wave-induced field opening.

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arXiv2026

The Role of Reconnection at Magnetic Separators in Complex Solar Flare Ribbons

Solar flare ribbons, manifesting as transient brightenings in the chromosphere, are believed to trace out the footpoints of magnetic field lines that are reconnecting higher in the solar atmosphere. These field lines lie in a separatrix or quasi-separatrix layer that separates domains of different magnetic connectivity and hence forms a natural location for reconnection. Solar flares are typically characterized as being circular ribbon flares, two-ribbon flares, or complex ribbon flares based on the number and shape of the ribbons. There are relatively well-developed models to explain the first two types of flares based on the location of the reconnection powering the flare. The case of complex ribbons is less well understood, but is often posited to be a result of reconnection at multiple locations. We demonstrate here that reconnection at a magnetic separator connecting two coronal null points can naturally explain the complex ribbons observed for two events, an M2.9 class flare from NOAA AR 11112 (SOL2010-10-16T19:12), and an X2.2 class flare from NOAA AR 11158 (SOL2011-02-15T01:56).

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arXiv2025

On the Uniqueness and Causal Relationship of Precursor Activity to Solar Energetic Events: I. Transient Brightenings -- Introduction and Overview

The physical role played by small-scale activity that occurs before the sudden onset of solar energetic events (SEEs, i.e., solar flares and coronal mass ejections) remains in question, in particular as related to SEE initiation and early evolution. It is still unclear whether such precursor activity, often interpreted as plasma heating, particle acceleration, or early filament activation, is indicative of a pre-event phase or simply on-going background activity. In this series, we statistically investigate the uniqueness and causal connection between precursors and SEEs using paired activity-quiet epochs. This first paper specifically introduces transient brightenings (TBs) and presents analysis regimes to study their role as precursors, including imaging of the solar atmosphere, magnetic field, and topology analysis. Applying these methods qualitatively to three cases, we find that prior to solar flares, TBs 1) tend to occur in one large cluster close to the future flare ribbon location and below the separatrix surface of a coronal magnetic null point, 2) are co-spatial with reconnection signatures in the lower solar atmosphere, such as bald patches and null point fan traces and 3) cluster in the vicinity of strong-gradient polarity inversion lines and regions of increased excess magnetic energy density. TBs are also observed during quiet epochs of the same active regions, but they appear in smaller clusters not following a clear spatial pattern, although sometimes associated with short, spatially-intermittent bald patches and fan traces, but predominantly away from strong gradient polarity inversion lines in areas with little excess energy density.

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arXiv2024

How does the critical torus instability height vary with the solar cycle?

The ideal magnetohydrodynamic torus instability can drive the eruption of coronal mass ejections. The critical threshold of magnetic field strength decay for the onset of the torus instability occurs at different heights in different solar active regions, and understanding this variation could therefore improve space weather prediction. In this work, we aim to find out how the critical torus instability height evolves throughout the solar activity cycle. We study a significant subset of HMI and MDI Space-Weather HMI Active Region Patches (SHARPs and SMARPs) from 1996-2023, totalling 21584 magnetograms from 4436 unique active region patches. For each magnetogram, we compute the critical height averaged across the main polarity inversion line, the total unsigned magnetic flux and the separation between the positive and negative magnetic polarities. We find the critical height in active regions varies with the solar cycle, with higher (lower) average critical heights observed around solar maximum (minimum). We conclude this is because the critical height is proportional to the separation between opposite magnetic polarities, which in turn is proportional to the total magnetic flux in a region, and more magnetic regions with larger fluxes and larger sizes are observed at solar maximum. This result is noteworthy because, despite the higher critical heights, more CMEs are observed around solar maximum than at solar minimum.

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arXiv2023

Implications of Different Solar Photospheric Flux-Transport Models for Global Coronal and Heliospheric Modeling

The concept of surface-flux transport (SFT) is commonly used in evolving models of the large-scale solar surface magnetic field. These photospheric models are used to determine the large-scale structure of the overlying coronal magnetic field, as well as to make predictions about the fields and flows that structure the solar wind. We compare predictions from two SFT models for the solar wind, open magnetic field footpoints, and the presence of coronal magnetic null points throughout various phases of a solar activity cycle, focusing on the months of April in even-numbered years between 2012 and 2020, inclusive. We find that there is a solar cycle dependence to each of the metrics considered, but there is not a single phase of the cycle in which all the metrics indicate good agreement between the models. The metrics also reveal large, transient differences between the models when a new active region is rotating into the assimilation window. The evolution of the surface flux is governed by a combination of large scale flows and comparatively small scale motions associated with convection. Because the latter flows evolve rapidly, there are intervals during which their impact on the surface flux can only be characterized in a statistical sense, thus their impact is modeled by introducing a random evolution that reproduces the typical surface flux evolution. We find that the differences between the predicted properties are dominated by differences in the model assumptions and implementation, rather than selection of a particular realization of the random evolution.

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arXiv2022

Properties of Flare-Imminent versus Flare-Quiet Active Regions from the Chromosphere through the Corona II: NonParametric Discriminant Analysis Results from the NWRA Classification Infrastructure (NCI)

A large sample of active-region-targeted time-series images from the Solar Dynamics Observatory / Atmospheric Imaging Assembly, the AIA Active Region Patch database ("AARPs", Paper I: Dissauer et al 2022) is used to investigate whether parameters describing the coronal, transition region, and chromospheric emission can differentiate a region that will imminently produce a solar flare from one that will not. Parametrizations based on moment analysis of direct and running-difference images provide for physically-interpretable results from nonparametric discriminant analysis. Across four event definitions including both 24hr and 6hr validity periods, 160 image-based parameters capture the general state of the atmosphere, rapid brightness changes, and longer-term intensity evolution. We find top Brier Skill Scores in the 0.07--0.33 range, True Skill Statistics in the 0.68--0.82 range (both depending on event definition), and Receiver Operating Characteristic Skill Scores above 0.8. Total emission can perform notably as can steeply increasing or decreasing brightness, although mean brightness measures do not, demonstrating the well-known active-region-size/flare-productivity relation. Once a region is flare productive, the active-region coronal plasma appears to stay hot. The 94AA filter data provides the most parameters with discriminating power, with indications that it benefits from sampling multiple physical regimes. In particular, classification success using higher-order moments of running difference images indicate a propensity for flare-imminent regions to display short-lived small-scale brightening events. Parameters describing the evolution of the corona can provide flare-imminent indicators, but at no preference over "static" parameters. Finally, all parameters and NPDA-derived probabilities are available to the community for additional research.

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arXiv2022

Large-Scale Spatial Cross-Calibration of Hinode/SOT-SP and SDO/HMI

We investigate the cross-calibration of the Hinode/SOT-SP and SDO/HMI instrument meta-data, specifically the correspondence of the scaling and pointing information. Accurate calibration of these datasets gives the correspondence needed by inter-instrument studies and learning-based magnetogram systems, and is required for physically-meaningful photospheric magnetic field vectors. We approach the problem by robustly fitting geometric models on correspondences between images from each instrument's pipeline. This technique is common in computer vision, but several critical details are required when using scanning slit spectrograph data like Hinode/SOT-SP. We apply this technique to data spanning a decade of the Hinode mission. Our results suggest corrections to the published Level 2 Hinode/SOT-SP data. First, an analysis on approximately 2,700 scans suggests that the reported pixel size in Hinode/SOT-SP Level 2 data is incorrect by around 1%. Second, analysis of over 12,000 scans show that the pointing information is often incorrect by dozens of arcseconds with a strong bias. Regression of these corrections indicates that thermal effects have caused secular and cyclic drift in Hinode/SOT-SP pointing data over its mission. We offer two solutions. First, direct co-alignment with SDO/HMI data via our procedure can improve alignments for many Hinode/SOT-SP scans. Second, since the pointing errors are predictable, simple post-hoc corrections can substantially improve the pointing. We conclude by illustrating the impact of this updated calibration on derived physical data products needed for research and interpretation. Among other things, our results suggest that the pointing errors induce a hemispheric bias in estimates of radial current density.

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arXiv2021

SynthIA: A Synthetic Inversion Approximation for the Stokes Vector Fusing SDO and Hinode into a Virtual Observatory

Both NASA's Solar Dynamics Observatory (SDO) and the JAXA/NASA Hinode mission include spectropolarimetric instruments designed to measure the photospheric magnetic field. SDO's Helioseismic and Magnetic Imager (HMI) emphasizes full-disk high-cadence and good spatial resolution data acquisition while Hinode's Solar Optical Telescope Spectro-Polarimeter (SOT-SP) focuses on high spatial resolution and spectral sampling at the cost of a limited field of view and slower temporal cadence. This work introduces a deep-learning system named SynthIA (Synthetic Inversion Approximation), that can enhance both missions by capturing the best of each instrument's characteristics. We use SynthIA to produce a new magnetogram data product, SynodeP (Synthetic Hinode Pipeline), that mimics magnetograms from the higher spectral resolution Hinode/SOT-SP pipeline, but is derived from full-disk, high-cadence, and lower spectral-resolution SDO/HMI Stokes observations. Results on held-out data show that SynodeP has good agreement with the Hinode/SOT-SP pipeline inversions, including magnetic fill fraction, which is not provided by the current SDO/HMI pipeline. SynodeP further shows a reduction in the magnitude of the 24-hour oscillations present in the SDO/HMI data. To demonstrate SynthIA's generality, we show the use of SDO/AIA data and subsets of the HMI data as inputs, which enables trade-offs between fidelity to the Hinode/SOT-SP inversions, number of observations used, and temporal artifacts. We discuss possible generalizations of SynthIA and its implications for space weather modeling. This work is part of the NASA Heliophysics DRIVE Science Center (SOLSTICE) at the University of Michigan under grant NASA 80NSSC20K0600E, and will be open-sourced.

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arXiv2021

Fast and Accurate Emulation of the SDO/HMI Stokes Inversion with Uncertainty Quantification

The Helioseismic and Magnetic Imager (HMI) onboard NASA's Solar Dynamics Observatory (SDO) produces estimates of the photospheric magnetic field which are a critical input to many space weather modelling and forecasting systems. The magnetogram products produced by HMI and its analysis pipeline are the result of a per-pixel optimization that estimates solar atmospheric parameters and minimizes disagreement between a synthesized and observed Stokes vector. In this paper, we introduce a deep learning-based approach that can emulate the existing HMI pipeline results two orders of magnitude faster than the current pipeline algorithms. Our system is a U-Net trained on input Stokes vectors and their accompanying optimization-based VFISV inversions. We demonstrate that our system, once trained, can produce high-fidelity estimates of the magnetic field and kinematic and thermodynamic parameters while also producing meaningful confidence intervals. We additionally show that despite penalizing only per-pixel loss terms, our system is able to faithfully reproduce known systematic oscillations in full-disk statistics produced by the pipeline. This emulation system could serve as an initialization for the full Stokes inversion or as an ultra-fast proxy inversion. This work is part of the NASA Heliophysics DRIVE Science Center (SOLSTICE) at the University of Michigan, under grant NASA 80NSSC20K0600E, and has been open sourced.

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arXiv2020

A Comparison of Flare Forecasting Methods. IV. Evaluating Consecutive-Day Forecasting Patterns

A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active". Building on earlier studies in this series (Barnes et al. 2016; Leka et al. 2019a,b) in which we describe methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multi-day periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods, and conversely, of correctly predicting declining flare activity. We demonstrate these evaluation methods graphically and quantitatively as they provide both quick comparative evaluations and options for detailed analysis. For the testing interval 2016-2017, we determine the relative frequency distribution of two-day dichotomous forecast outcomes for three different event histories (i.e., event/event, no-event/event and event/no-event), and use it to highlight performance differences between forecasting methods. A trend is identified across all forecasting methods that a high/low forecast probability on day-1 remains high/low on day-2 even though flaring activity is transitioning. For M-class and larger flares, we find that explicitly including persistence or prior flare history in computing forecasts helps to improve overall forecast performance. It is also found that using magnetic/modern data leads to improvement in catching the first-event/first-no-event transitions. Finally, 15% of major (i.e., M-class or above) flare days over the testing interval were effectively missed due to a lack of observations from instruments away from the Earth-Sun line.

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arXiv2019

A Comparison of Flare Forecasting Methods. III. Systematic Behaviors of Operational Solar Flare Forecasting Systems

A workshop was recently held at Nagoya University (31 October - 02 November 2017), sponsored by the Center for International Collaborative Research, at the Institute for Space-Earth Environmental Research, Nagoya University, Japan, to quantitatively compare the performance of today's operational solar flare forecasting facilities. Building upon Paper I of this series (Barnes et al. 2016), in Paper II (Leka et al. 2019) we described the participating methods for this latest comparison effort, the evaluation methodology, and presented quantitative comparisons. In this paper we focus on the behavior and performance of the methods when evaluated in the context of broad implementation differences. Acknowledging the short testing interval available and the small number of methods available, we do find that forecast performance: 1) appears to improve by including persistence or prior flare activity, region evolution, and a human "forecaster in the loop"; 2) is hurt by restricting data to disk-center observations; 3) may benefit from long-term statistics, but mostly when then combined with modern data sources and statistical approaches. These trends are arguably weak and must be viewed with numerous caveats, as discussed both here and in Paper II. Following this present work, we present in Paper IV a novel analysis method to evaluate temporal patterns of forecasting errors of both types (i.e., misses and false alarms; Park et al. 2019). Hence, most importantly, with this series of papers we demonstrate the techniques for facilitating comparisons in the interest of establishing performance-positive methodologies.

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arXiv2019

A Comparison of Flare Forecasting Methods. II. Benchmarks, Metrics and Performance Results for Operational Solar Flare Forecasting Systems

Solar flares are extremely energetic phenomena in our Solar System. Their impulsive, often drastic radiative increases, in particular at short wavelengths, bring immediate impacts that motivate solar physics and space weather research to understand solar flares to the point of being able to forecast them. As data and algorithms improve dramatically, questions must be asked concerning how well the forecasting performs; crucially, we must ask how to rigorously measure performance in order to critically gauge any improvements. Building upon earlier-developed methodology (Barnes et al, 2016, Paper I), international representatives of regional warning centers and research facilities assembled in 2017 at the Institute for Space-Earth Environmental Research, Nagoya University, Japan to - for the first time - directly compare the performance of operational solar flare forecasting methods. Multiple quantitative evaluation metrics are employed, with focus and discussion on evaluation methodologies given the restrictions of operational forecasting. Numerous methods performed consistently above the "no skill" level, although which method scored top marks is decisively a function of flare event definition and the metric used; there was no single winner. Following in this paper series we ask why the performances differ by examining implementation details (Leka et al. 2019, Paper III), and then we present a novel analysis method to evaluate temporal patterns of forecasting errors in (Park et al. 2019, Paper IV). With these works, this team presents a well-defined and robust methodology for evaluating solar flare forecasting methods in both research and operational frameworks, and today's performance benchmarks against which improvements and new methods may be compared.

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arXiv2018

The NWRA Classification Infrastructure: Description and Extension to the Discriminant Analysis Flare Forecasting System (DAFFS)

A classification infrastructure built upon Discriminant Analysis has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling "null" and "bad" data in multi-parameter analysis, application of non-parametric multi-dimensional Discriminant Analysis, an extension through Bayes' theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of "Research to Operations" efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.

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arXiv2018

Does Nearby Open Flux Affect the Eruptivity of Solar Active Regions?

The most energetic solar flares are typically associated with the ejection of a cloud of coronal material into the heliosphere in the form of a coronal mass ejection (CME). However, there exist large flares which are not accompanied by a CME. The existence of these non-eruptive flares raises the question of whether such flares suffer from a lack of access to nearby open fields in the vicinity above the flare (reconnection) site. In this study, we use a sample of 56 flares from Sunspot Cycles 23 and 24 to test whether active regions that produce eruptive X-class flares are preferentially located near coronal magnetic field domains that are open to the heliosphere, as inferred from a potential field source surface model. The study shows that X-class flares having access to open fields are eruptive at a higher rate than those for which access is lacking. The significance of this result should be moderated due to the small number of non-eruptive X-class flares in the sample, based on the associated Bayes factor.

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arXiv2015

Achieving Consistent Doppler Measurements from SDO/HMI Vector Field Inversions

NASA's Solar Dynamics Observatory is delivering vector field observations of the full solar disk with unprecedented temporal and spatial resolution; however, the satellite is in a highly inclined geostationary orbit. The relative spacecraft-Sun velocity varies by $\pm3$~km/s over a day which introduces major orbital artifacts in the Helioseismic Magnetic Imager data. We demonstrate that the orbital artifacts contaminate all spatial and temporal scales in the data. We describe a newly-developed three stage procedure for mitigating these artifacts in the Doppler data derived from the Milne-Eddington inversions in the HMI Pipeline. This procedure was applied to full disk images of AR11084 to produce consistent Dopplergrams. The data adjustments reduce the power in the orbital artifacts by 31dB. Furthermore, we analyze in detail the corrected images and show that our procedure greatly improve the temporal and spectral properties of the data without adding any new artifacts. We conclude that this new and easily implemented procedure makes a dramatic improvement in the consistency of the HMI data and in its usefulness for precision scientific studies.

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arXiv2014

The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Overview and Performance

The Helioseismic and Magnetic Imager (HMI) began near-continuous full-disk solar measurements on 1 May 2010 from the Solar Dynamics Observatory (SDO). An automated processing pipeline keeps pace with observations to produce observable quantities, including the photospheric vector magnetic field, from sequences of filtergrams. The primary 720s observables were released in mid 2010, including Stokes polarization parameters measured at six wavelengths as well as intensity, Doppler velocity, and the line-of-sight magnetic field. More advanced products, including the full vector magnetic field, are now available. Automatically identified HMI Active Region Patches (HARPs) track the location and shape of magnetic regions throughout their lifetime. The vector field is computed using the Very Fast Inversion of the Stokes Vector (VFISV) code optimized for the HMI pipeline; the remaining 180 degree azimuth ambiguity is resolved with the Minimum Energy (ME0) code. The Milne-Eddington inversion is performed on all full-disk HMI observations. The disambiguation, until recently run only on HARP regions, is now implemented for the full disk. Vector and scalar quantities in the patches are used to derive active region indices potentially useful for forecasting; the data maps and indices are collected in the SHARP data series, hmi.sharp_720s. Patches are provided in both CCD and heliographic coordinates. HMI provides continuous coverage of the vector field, but has modest spatial, spectral, and temporal resolution. Coupled with limitations of the analysis and interpretation techniques, effects of the orbital velocity, and instrument performance, the resulting measurements have a certain dynamic range and sensitivity and are subject to systematic errors and uncertainties that are characterized in this report.

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arXiv2014

The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: SHARPs -- Space-weather HMI Active Region Patches

A new data product from the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO) called Space-weather HMI Active Region Patches (SHARPs) is now available. SDO/HMI is the first space-based instrument to map the full-disk photospheric vector magnetic field with high cadence and continuity. The SHARP data series provide maps in patches that encompass automatically tracked magnetic concentrations for their entire lifetime; map quantities include the photospheric vector magnetic field and its uncertainty, along with Doppler velocity, continuum intensity, and line-of-sight magnetic field. Furthermore, keywords in the SHARP data series provide several parameters that concisely characterize the magnetic-field distribution and its deviation from a potential-field configuration. These indices may be useful for active-region event forecasting and for identifying regions of interest. The indices are calculated per patch and are available on a twelve-minute cadence. Quick-look data are available within approximately three hours of observation; definitive science products are produced approximately five weeks later. SHARP data are available at http://jsoc.stanford.edu and maps are available in either of two different coordinate systems. This article describes the SHARP data products and presents examples of SHARP data and parameters.

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arXiv2011

Response to "Comment on `Resolving the 180deg Ambiguity in Solar Vector Magnetic Field Data: Evaluating the Effects of Noise, Spatial Resolution, and Method Assumptions'"

We address points recently discussed in Georgoulis (2011) in reference to Leka et al. (2009b). Most importantly, we find that the results of Georgoulis (2011) support a conclusion of Leka et al. (2009b): that limited spatial resolution and the presence of unresolved magnetic structures can challenge ambiguity- resolution algorithms. Moreover, the findings of both Metcalf et al. (2006) and Leka et al. (2009b) are confirmed in Georgoulis (2011): a method's performance can be diminished when the observed field fails to conform to that method's assumptions. The implication of boundaries in models of solar magnetic structures is discussed; we confirm that the distribution of the field components in the model used in Leka et al. (2009b) is closer to what is observed on the Sun than what is proposed in Georgoulis (2011). It is also shown that method does matter with regards to simulating limited spatial resolution and avoiding an inadvertent introduction of bias. Finally, the assignment of categories to data- analysis algorithms is revisited; we argue that assignments are only useful and elucidating when used appropriately.

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