K. D. Leka

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 farside 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 Solar Terrestrial Relations Observatory (STEREO)/Extreme Ultraviolet Imager (EUVI) 304 Å 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 active region (AR) based on descriptors of magnetic flux, field strength, and location. The model achieves a precision of 0.93 for 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 nearside-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 the full-Sun magnetic field.

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 “” 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 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.

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.

Solar photospheric line-of-sight magnetograms are easier to estimate than full vector magnetograms since the line-of-sight component ( B _los ) can be obtained from total intensity and circular polarization signals, unlike the perpendicular component ( B _⊥ ), which depends on harder-to-measure linear polarization. Unfortunately, the line-of-sight component by itself is not physically meaningful, as it is just one component of the underlying vector and one whose relationship to the gravity vector changes pixel-to-pixel. To produce an estimate of the radial component ( B _R ), a common “correction” is often applied that assumes the field is radial, which is nearly always false. This paper investigates recovering full vector field information from B _los by building on the recent SuperSynthIA approach that was originally used with Stokes vectors as input for simultaneous inversion and disambiguation. As input, the method accepts one or more line-of-sight magnetograms and associated metadata; as output, our method estimates full vector field in heliographic components, meaning that the physically relevant vector components are returned without need for further disambiguation steps or component transforms. We demonstrate the ability to produce good estimates of the full vector field on unseen examples from both HMI and GONG, including examples that predate the Solar Dynamics Observatory mission. Our results show that learning is not a replacement for a dedicated vector-field observing facilities, but may serve to unlock information from past data and at the very least, provide more accurate B _R maps from B _los than are created using the simple viewing angle assumption.

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 ongoing 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 cospatial 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.

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.

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 a 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 a solar eruption, we simulated the first of several large flares in NOAA active region 11283. We used a data-driven model that was governed by zero-beta MHD, focusing on the free magnetic energy accumulation prior to the M5.3 flare (2011 September 6 at 01:59 UT). 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.

Vector magnetograms of the Sun’s photosphere are cornerstones for much of solar physics research. These data are often produced by data-analysis pipelines combining per-pixel Stokes polarization vector inversion with a disambiguation that resolves an intrinsic 180° ambiguity. We introduce a learning-based method, SuperSynthIA, that produces full-disk vector magnetograms from Stokes vector observations. As input, SuperSynthIA uses Stokes polarization images from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI). As output, SuperSynthIA simultaneously emulates the inversion and disambiguation outputs from the Hinode/Solar Optical Telescope-Spectro-Polarimeter (SOT-SP) pipeline. Our method extends our previous approach SynthIA with heliographic outputs as well as using an improved data set and inference method. SuperSynthIA provides a new tool for improved magnetic fields from full-disk SDO/HMI observations using information derived from the enhanced capabilities of Hinode/SOT-SP. Compared to our previous SynthIA, SuperSynthIA provides physics-ready vector magnetograms and mitigates unphysical angle preferences and banding artifacts in SynthIA. SuperSynthIA data are substantially more temporally consistent than those from the SDO/HMI pipeline, most notably seen in, e.g., evolving active regions. SuperSynthIA substantially reduces noise in low-signal areas, resulting in less center-to-limb bias outside of strong-signal areas. We show that outputs from SuperSynthIA track the SDO/HMI-recorded evolution of the magnetic field. We discuss the limitations of SuperSynthIA that the user must understand, and we demonstrate a broad set of evaluations to test SuperSynthIA and discuss remaining known artifacts. Our tests provide both methodology and evidence that SuperSynthIA outputs are ready for use by the community, and that learning-based approaches are suitable for physics-ready magnetograms.

Whitepaper #320 in the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033. Main topics: space weather applications; basic research. Additional topics: ground-based missions/projects; solar physics; other space weather; user-need-driven basic space weather […]