A Probabilistic Framework for Incorporating Helioseismic Far-side Active Regions into Surface Flux Transport Models
5 affiliations
- 1Southwest Research Institute
- 2Northwest Research Associates
- 3Stanford University
- 4Naval Research Laboratory
- 5National Solar Observatory
Abstract
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.
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BibTeX
@article{jha2026probabilistic,
title = {{A Probabilistic Framework for Incorporating Helioseismic Far-side Active Regions into Surface Flux Transport Models}},
author = {Bibhuti Kumar Jha and Lisa A. Upton and K. D. Leka and Ruizhu Chen and Junwei Zhao and Shea Hess Webber and Ignacio Ugarte-Urra and Kiran Jain},
journal = {The Astrophysical Journal Supplement Series},
year = {2026},
month = oct,
doi = {10.3847/1538-4365/aea139},
url = {https://iopscience.iop.org/article/10.3847/1538-4365/aea139},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A Probabilistic Framework for Incorporating Helioseismic Far-side Active Regions into Surface Flux Transport Models
AU - Bibhuti Kumar Jha
AU - Lisa A. Upton
AU - K. D. Leka
AU - Ruizhu Chen
AU - Junwei Zhao
AU - Shea Hess Webber
AU - Ignacio Ugarte-Urra
AU - Kiran Jain
PY - 2026
DA - 2026/10/08
JO - The Astrophysical Journal Supplement Series
DO - 10.3847/1538-4365/aea139
UR - https://iopscience.iop.org/article/10.3847/1538-4365/aea139
AB - 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.
SN - 0067-0049
SN - 1538-4365
ER -