Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.34510

Magnetic Topological Evolution and the Predictive Role of Winding-flux-based Masked PILs in Extreme Solar Flares from AR13664 and AR13842

Prithish Halder·Sudheer K. Mishra·Abhishekh K. Srivastava·Ayumi Asai·Peter A. Dowben
View original source ↗Read source document ↗Export metadata ↓

Abstract

Using vector magnetogram data from the Helioseismic Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO), we present detailed case studies of precursor activity in Active Regions (ARs) 13664 and 13842, which produced several intense flares. We examined the spatiotemporal evolution of key magnetic topological parameters, including winding, helicity, winding-flux-based masked polarity inversion lines (PILs), and PIL gradients, from the precursor to the post-flare phases. The temporal evolution of the winding flux showed a significant enhancement during the precursor phase, and this trend was consistent across the seven X-class and five M-class flares (>= M5.0). The spatial distribution of both raw and Gaussian-smoothed winding-flux enhancement is co-located with flare footpoint regions of highly twisted core fields, as indicated by the photospheric magnetic topology. The winding-flux-based masked PILs that persisted around the flare locations showed a significant increase in strength and area during the precursor activity. These masked PILs spatially coincide with regions likely hosting non-potential core magnetic fields associated with the observed flares. Furthermore, the gradient of strong PILs extracted from the ARs was positively correlated with flare-associated locations and typically increased several hours (approx. 3-5 h) before flare onset. By imposing a gradient threshold on strong PILs, we isolate magnetic regions repeatedly associated with flare initiation, providing useful constraints in regimes where current flare-forecasting models remain uncertain. Therefore, the combined analysis of the spatiotemporal evolution of different magnetic topological parameters enhances our understanding of intense solar flare forecasting and the identification of potential triggering sites, thereby contributing to improved space weather prediction frameworks.

Explore related subjects

astro-ph.SRastro-ph.IMphysics.geo-phphysics.space-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Prithish Halder, Sudheer K. Mishra, Abhishekh K. Srivastava, Ayumi Asai, Peter A. Dowben. 2026-10-25. Magnetic Topological Evolution and the Predictive Role of Winding-flux-based Masked PILs in Extreme Solar Flares from AR13664 and AR13842. https://arxiv.org/abs/2610.34510

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Machine Learning Applications in the Solar Interior and Atmosphere

The solar interior and atmosphere constitute a strongly coupled, multiscale dynamical system defined by continuous temporal evolution. Predicting the state of this system requires modeling physical interactions that span disparate spatial and temporal scales, constrained by heterogeneous and sparse data. This chapter formalizes the Sun within the mathematical framework of dynamical systems theory, defining a global state vector ($\vec{S}$) governed by thermodynamical, kinetic, and magnetic properties. Because solar observations represent projected, multi-modal, and partially observable measurements ($\vec{O}[\vec{S}]$) of this hidden state, pure physics-based deduction is fundamentally constrained. We present a unifying thesis: the primary role of machine learning in heliophysics is to act as a structured family of mathematical operators---specifically Inference ($\mathcal{I}$), Emulation ($\mathcal{E}$), Propagation ($\mathcal{P}$), and Control ($\mathcal{C}$)---that reconstruct, simulate, and couple this global state vector across disparate physical layers. By framing the vast, multi-disciplinary literature within this unified state-space taxonomy, we demonstrate how data-driven techniques bridge observational gaps to resolve hidden multiscale couplings and dramatically accelerate the simulation of non-linear processes, establishing a rigorous pathway to predict the global evolution of the Sun.

astro-ph.SR↗
arXiv2026

Gaia DR3 Compact Binary Candidates Constrained by Radial Velocity Measurements

Astrometry enables the most direct determination of the orbital elements and component masses of compact binaries. The potential of the current Gaia data for this purpose has not yet been fully explored. In this work, we combine Gaia astrometric and radial velocity (RV) measurements to perform joint orbital fitting for 1,835 Gaia astrometric binaries with available RV data. Among the 518 systems with at least three effective RV epochs, we identify 171 compact binary candidates, including four with inferred dark companion masses exceeding 2.1 $M_{\odot}$ and 22 between 1.4 and 2.1 $M_{\odot}$. These candidates provide valuable targets for future follow-up observations and Gaia DR4 analyses. We also flag 1,317 additional potential compact binary candidates with effective RV epochs less than three, including 84 with inferred companion masses above 2.1 $M_{\odot}$ and 173 between 1.4 and 2.1 $M_{\odot}$. More importantly, our results point to a large, currently hidden population of black hole and neutron star binaries that can be further explored with the forthcoming Gaia DR4 data and complementary follow-up observations.

astro-ph.SR↗
arXiv2026

Multiplicity of Massive stars in the Milky Way (M3W). II. The brightest single O stars: variability and systemic velocities

(ABRIDGED) M3W studies the dynamical properties of massive stars in the solar vicinity, such as their systemic velocities, which are affected by variability effects: pulsations, line infilling by wind and disks, and incomplete knowledge about the central wavelengths. We want to increase the accuracy of the O-stars systemic velocities. We establish how different wavelength regions are affected by variability, determine the thresholds for the detection of spectroscopic binarity, and obtain accurate central wavelengths including the effects of contamination, forbidden lines, and wind infilling. We analyse 41 apparently single bright O stars. We used multi-epoch HR LiLiMaRlin spectroscopy cleaned with UNWIND and stellar models computed with FASTWIND. The epoch spectra were cross-correlated to study their variability. The RVs were computed from (a) cross-correlation of H and He lines with FASTWIND models, (b) profile fitting of metallic lines, and (c) cross-correlation of the full spectrum with a template star. A comparison between the RV dispersions for different wavelength regions reveals different groups of stars in terms of variability: (a) non-SG slow rotators, (b) fast rotators, (c) SGs, and (d) Oe stars. This is a manifestation of the different role of pulsations, winds, and disks in RV variability. We determine RV dispersion thresholds for the detection of new SBs. We also use a FASTWIND grid to establish the effects of (a) the presence of He II lines close to Balmer lines, (b) the existence of forbidden components in two He I lines, and (c) wind infilling in the determination of accurate RVs. With all of that, we determined RVs for our 41 stars, checked their accuracy, and discovered significant differences with literature values. Our analysis has further revealed four SB1 systems unknown in the summer of 2025, one new SB2, two new Oe stars, and three new Onfp stars.

astro-ph.SR↗