arXiv subjects

K. D. Leka

Publications and source records attributed to K. D. Leka.

At least 19 recordsLinked to original sources

arXiv2026

A Probabilistic Framework for Incorporating Helioseismic Far-Side Active Regions into SFT Models

Surface Flux Transport (SFT) models are routinely used to model the Sun's photospheric magnetic field and provide inner boundary conditions for coronal and heliospheric models, yet they remain fundamentally limited by the lack of direct information about flux emergence on the far side of the Sun. To address this, we develop a framework for incorporating helioseismic images of far-side active regions (HIFAR), into the Advective Flux Transport (AFT) model. Using near-simultaneous magnetic flux maps (HIFARM) inferred from HIFAR, and STEREO/EUVI 304 A observations from 2010 May 13 to 2014 August 18, we develop a logistic regression model to estimate the probability that a helioseismic detection corresponds to an AR based on descriptors of magnetic flux, field strength, and location. The model achieves a precision of 0.93 for active region (AR) detections at a probability threshold of 0.73, selected to reject 90% of "ghost" ARs. We also derive an empirical scaling relationship between HIFARM and AFT flux to place HIFARM on the AFT flux scale and demonstrate that the framework reproduces the flux evolution of ARs consistent with near-side observations over multiple solar rotations. At the global scale, we find that conventional near-side-driven AFT simulations underestimate the total unsigned magnetic flux by ~10-20% of the Sun's total magnetic flux budget. The probabilistic AR detection model and the complete framework developed in this work provide a practical pathway for incorporating helioseismic far-side ARs into SFT models, a step toward realistic modeling of full-Sun magnetic field.

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arXiv2026

Addressing Known Challenges in Solar Flare Forecasting I: Limb-Flare Prediction with a 4-pi Full-Heliosphere Framework

A demonstrated failure mode for operational solar flare forecasting is the inability to forecast flares that occur near, or just beyond, the solar limb. To address this shortcoming, we develop a "4pi" full-heliosphere event forecasting framework and evaluate its statistical classification ability against this specific challenge. A magnetic surface flux transport model is used to generate full-sun maps of the photospheric radial magnetic field from which active regions (ARs) are identified and tracked using a new labeling scheme that is observer-location agnostic and allows for post-facto modifications. Flare-relevant magnetic parameters couple to a "visibility" index that specifies AR location relative to the visible solar limb and expected flare detection. Flare labels are assigned according to peak Soft X-ray flux, and a statistical classification is performed using nonparametric discriminant analysis. A version where new or emerging ARs on the far ("invisible" side of the Sun are incorporated into the model by way of far-side helioseismology, is also tested. We evaluate the new framework by its performance specifically including the limb areas using Brier Skill Score and ROC Skill Score, finding improvement at the 2-sigma level or less. However, we do find that the number of False Negatives, or "missed" forecasts decreases, and find strong evidence that the additional information provided by the far-side helioseismology can help predict near- and just-beyond-limb flares, particularly for East-limb events. While individual components of this framework could be improved, we demonstrate that a known failure mode for solar flare forecasting can be mitigated with available resources.

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arXiv2025

Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme

Conditional diffusion models provide a natural framework for probabilistic prediction of dynamical systems and have been successfully applied to fluid dynamics and weather prediction. However, in many settings, the available information at a given time represents only a small fraction of what is needed to predict future states, either due to measurement uncertainty or because only a small fraction of the state can be observed. This is true for example in solar physics, where we can observe the Sun's surface and atmosphere, but its evolution is driven by internal processes for which we lack direct measurements. In this paper, we tackle the probabilistic prediction of partially observable, long-memory dynamical systems, with applications to solar dynamics and the evolution of active regions. We show that standard inference schemes, such as autoregressive rollouts, fail to capture long-range dependencies in the data, largely because they do not integrate past information effectively. To overcome this, we propose a multiscale inference scheme for diffusion models, tailored to physical processes. Our method generates trajectories that are temporally fine-grained near the present and coarser as we move farther away, which enables capturing long-range temporal dependencies without increasing computational cost. When integrated into a diffusion model, we show that our inference scheme significantly reduces the bias of the predicted distributions and improves rollout stability.

cs.LG↗

arXiv2024

Data-driven MHD Simulation of the Formation of a Magnetic Flux Rope and an Inclined Solar Eruption

Solar energetic events are caused by the release of magnetic energy accumulated in the solar atmosphere. To understand their initiating physical mechanisms, the dynamics of the coronal magnetic fields must be studied. Unfortunately, the dominant mechanisms are still unclear due to lack of direct measurements. Numerical simulations based on magnetohydrodynamics (MHD) can reproduce the dynamical evolution of solar coronal magnetic field providing a useful tool to explore flare initiation. Data-driven MHD simulations, in which the time-series observational data of the photospheric magnetic field is used as the simulation boundary condition, can explore different mechanisms. To investigate the accumulation of free magnetic energy through to a solar eruption, we simulated the first of several large flares in NOAA Active Region 11283. We used a data-driven model (Kaneko et al 2021) that was governed by zero-beta MHD, focusing on the free magnetic energy accumulation prior to the M5.3 flare (September 6, 01:59 UT, 2011). We reproduced the flare-associated eruption following the formation of twisted magnetic fields, or a magnetic flux rope (MFR), formed by photospheric motions at its footpoints. We found that the eruption was first triggered by the growth of the torus instability. The erupting MFR caused magnetic reconnections with neighboring magnetic field lines located along the direction of the eruption. Using the simulation results and an axial-radial decay index centered on the MFR, we find a natural explanation for the inclination of the eruption and a possible approach to predict the direction of solar eruptive events.

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arXiv2022

Helio2024 Science White Paper: ngGONG -- Future Ground-based Facilities for Research in Heliophysics and Space Weather Operational Forecast

Long-term synoptic observations of the Sun are critical for advancing our understanding of Sun as an astrophysical object, understanding the solar irradiance and its role in solar-terrestrial climate, for developing predictive capabilities of solar eruptive phenomena and their impact on our home planet, and heliosphere in general, and as a data provider for the operational space weather forecast. We advocate for the development of a ground-based network of instruments provisionally called ngGONG to maintain critical observing capabilities for synoptic research in solar physics and for the operational space weather forecast.

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arXiv2022

Seismic Monitoring of the Sun's Far Hemisphere: A Crucial Component in Future Space Weather Forecasting (A White Paper Submitted to the Decadal Survey for Solar and Space Physics (Heliophysics) -- SSPH 2024-2033)

The purpose of this white paper is to put together a coherent vision for the role of helioseismic monitoring of magnetic activity in the Sun's far hemisphere that will contribute to improving space weather forecasting as well as fundamental research in the coming decade. Our goal fits into the broader context of helioseismology in solar research for any number of endeavors when helioseismic monitors may be the sole synoptic view of the Sun's far hemisphere. It is intended to foster a growing understanding of solar activity, as realistically monitored in both hemispheres, and its relationship to all known aspects of the near-Earth and terrestrial environment. Some of the questions and goals that can be fruitfully pursued through seismic monitoring of farside solar activity in the coming decade include: What is the relationship between helioseismic signatures and their associated magnetic configurations, and how is this relationship connected to the solar EUV irradiance over the period of a solar rotation?; How can helioseismic monitoring contribute to data-driven global magnetic-field models for precise space weather forecasting?; What can helioseismic monitors tell us about prospects of a flare, CME or high-speed stream that impacts the terrestrial environment over the period of a solar rotation?; How does the inclusion of farside information contribute to forecasts of interplanetary space weather and the environments to be encountered by human crews in interplanetary space? Thus, it is crucial for the development of farside monitoring of the Sun be continued into the next decade either through ground-based or space-borne observations.

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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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arXiv2022

Multi-height Measurements Of The Solar Vector Magnetic Field: A White Paper Submitted To The Decadal Survey For Solar And Space Physics (Heliophysics) 2024-2033

This white paper advocates the importance of multi-height measurements of the vector magnetic field in the solar atmosphere. As briefly described in this document, these measurements are critical for addressing some of the most fundamental questions in solar and heliospheric physics today, including: (1) What is the origin of the magnetic field observed in the solar atmosphere? (2) What is the coupling between magnetic fields and flows throughout the solar atmosphere? Accurate measurements of the photospheric and chromospheric three-dimensional magnetic fields are required for a precise determination of the emergence and evolution of active regions. Newly emerging magnetic flux in pre-existing magnetic regions causes an increase in the topological complexity of the magnetic field, which leads to flares and coronal mass ejections. Measurements of the vector magnetic field constitute also the primary product for space weather operations, research, and modeling of the solar atmosphere and heliosphere. The proposed next generation Ground-based solar Observing Network Group (ngGONG), a coordinated system of multi-platform instruments, will address these questions and provide large datasets for statistical investigations of solar feature behavior and evolution and continuity in monitoring for space-weather focused endeavors both research and operational. It will also enable sun-as-a-star investigations, crucial as we look toward understanding other planet-hosting stars.

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arXiv2022

On Identifying and Mitigating Bias in Inferred Measurements for Solar Vector Magnetic Field Data

The problem of bias, meaning over- or underestimation, of the component perpendicular to the line-of-sight, Bperp, in vector magnetic field maps is discussed. Previous works on this topic have illustrated that the problem exists; here we perform novel investigations to quantify the bias, fully understand its source(s), and provide mitigation strategies. First, we develop quantitative metrics to measure the Bperp bias and quantify the effect in both local (physical) and native image-plane components. Second we test and evaluate different inversion options and data sources, to systematically characterize the impacts of choices, including explicitly accounting for the magnetic fill fraction ff. Third we deploy a simple model to test how noise and different models of the bias may manifest. From these three investigations we find that while the bias is dominantly present in under-resolved structures, it is also present in strong-field pixel-filling structures. Noise in the magnetograms can exacerbate the problem, but it is not the primary cause. We show that fitting ff explicitly provides significant mitigation, but that other considerations such as choice of chi^2 weights and optimization algorithms can impact the results as well. Finally, we demonstrate a straightforward "quick fix" that can be applied post-facto but prior to solving the 180deg ambiguity in Bperp, and which may be useful when global-scale structures are, e.g., used for model boundary input. The conclusions of this work support the deployment of inversion codes that explicitly fit ff or, as with the new SyntHIA neural-net, that are trained on data that did so.

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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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arXiv2021

Magnetic Helicity Flux across Solar Active Region Photospheres: II. Association of Hemispheric Sign Preference with Flaring Activity during Solar Cycle 24

In our earlier study of this series (Park et al. 2020, Paper I), we examined the hemispheric sign preference (HSP) of magnetic helicity flux $dH/dt$ across photospheric surfaces of 4802 samples of 1105 unique active regions (ARs) observed during solar cycle 24. Here, we investigate any association of the HSP, expressed as a degree of compliance, with flaring activity, analyzing the same set of $dH/dt$ estimates as used in Paper I. The AR samples under investigation are assigned to heliographic regions (HRs) defined in the Carrington longitude-latitude plane with a grid spacing of 45$^\circ$ in longitude and 15$^\circ$ in latitude. For AR samples in each of the defined HRs, we calculate the degree of HSP compliance and the average soft X-ray flare index. The strongest flaring activity is found to be in one distinctive HR with an extremely low HSP compliance of 41% as compared to the mean and standard deviation of 62% and 7%, respectively, over all HRs. This sole HR shows an anti-HSP (i.e., less than 50%) and includes the highly flare-productive AR NOAA 12673, however this AR is not uniquely responsible for the HR's low HSP. We also find that all HRs with the highest flaring activity are located in the southern hemisphere, and they tend to have lower degrees of HSP compliance. These findings point to the presence of localized regions of the convection zone with enhanced turbulence, imparting a greater magnetic complexity and a higher flaring rate to some rising magnetic flux tubes.

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arXiv2021

On a limitation of Zeeman polarimetry and imperfect instrumentation in representing solar magnetic fields with weaker polarization signal

Full disk vector magnetic fields are used widely for developing better understanding of large-scale structure, morphology, and patterns of the solar magnetic field. The data are also important for modeling various solar phenomena. However, observations of vector magnetic fields have one important limitation that may affect the determination of the true magnetic field orientation. This limitation stems from our ability to interpret the differing character of the Zeeman polarization signals which arise from the photospheric line-of-sight vs. the transverse components of the solar vector magnetic field, and is likely exacerbated by unresolved structure (non-unity fill fraction) as well as the disambiguation of the 180$^\circ$ degeneracy in the transverse-field azimuth. Here we provide a description of this phenomenon, and discuss issues, which require additional investigation.

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arXiv2020

Magnetic Helicity Flux across Solar Active Region Photospheres: I. Hemispheric Sign Preference in Solar Cycle 24

A hemispheric preference in the dominant sign of magnetic helicity has been observed in numerous features in the solar atmosphere: i.e., left-handed/right-handed helicity in the northern/southern hemisphere. The relative importance of different physical processes which may contribute to the observed hemispheric sign preference (HSP) of magnetic helicity is still under debate. Here, we estimate magnetic helicity flux ($dH/dt$) across the photospheric surface for 4,802 samples of 1,105 unique active regions (ARs) that appeared over an 8-year period from 2010 to 2017 during solar cycle 24, using photospheric vector magnetic field observations by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). The estimates of $dH/dt$ show that 63% and 65% of the investigated AR samples in the northern and southern hemispheres, respectively, follow the HSP. We also find a trend that the HSP of $dH/dt$ increases from ~50-60% up to ~70-80% as ARs (1) appear at the earlier inclining phase of the solar cycle or higher latitudes; (2) have larger values of $|dH/dt|$, the total unsigned magnetic flux, and the average plasma flow speed. These observational findings support the enhancement of the HSP mainly by the Coriolis force acting on a buoyantly rising and expanding flux tube through the turbulent convection zone. In addition, the differential rotation on the solar surface as well as the tachocline $α$-effect of flux-transport dynamo may reinforce the HSP for ARs at higher latitudes.

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arXiv2020

On measuring divergence for magnetic field modeling

A physical magnetic field has a divergence of zero. Numerical error in constructing a model field and computing the divergence, however, introduces a finite divergence into these calculations. A popular metric for measuring divergence is the average fractional flux $\langle |f_{i}| \rangle$. We show that $\langle |f_{i}| \rangle$ scales with the size of the computational mesh, and may be a poor measure of divergence because it becomes arbitrarily small for increasing mesh resolution, without the divergence actually decreasing. We define a modified version of this metric that does not scale with mesh size. We apply the new metric to the results of DeRosa et al. (2015), who measured $\langle |f_{i}| \rangle$ for a series of Nonlinear Force-Free Field (NLFFF) models of the coronal magnetic field based on solar boundary data binned at different spatial resolutions. We compute a number of divergence metrics for the DeRosa et al. (2015) data and analyze the effect of spatial resolution on these metrics using a non-parametric method. We find that some of the trends reported by DeRosa et al. (2015) are due to the intrinsic scaling of $\langle |f_{i}| \rangle$. We also find that different metrics give different results for the same data set and therefore there is value in measuring divergence via several metrics.

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arXiv2020

The Trigger Mechanism of Recurrent Solar Active Region Jets Revealed by the Magnetic Properties of a Coronal Geyser Site

Solar active region jets are small-scale collimated plasma eruptions that are triggered from magnetic sites embedded in sunspot penumbral regions. Multiple trigger mechanisms for recurrent jets are under debate. Vector magnetic field data from SDO-HMI observations are used to analyze a prolific photospheric configuration, identified in extreme ultraviolet observations as a `Coronal Geyser', that triggered a set of at least 10 recurrent solar active region jets. We focus on interpreting the magnetic fields of small-scale flaring sites aiming to understand the processes that govern recurrent jet eruptions. We perform a custom reprocessing of the SDO-HMI products, including disambiguation and uncertainty estimation. We scrutinized the configuration and dynamics of the photospheric magnetic structures. The magnetic configuration is described via the analysis of the photospheric magnetic vertical fields, to identify the process is responsible for driving the jet eruptions. We report that the two widely debated magnetic trigger processes, namely magnetic flux cancellation and magnetic flux emergence, appear to be responsible on a case by case basis for generating each eruption in our set. We find that 4 of 10 jets were due to flux cancellation while the rest were clearly not, and were more likely due to flux emergence.

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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

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↗

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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