New Probabilistic Model Maps the Sun’s Hidden Far Side for Better Space Weather Forecasts

A team of scientists led by the Southwest Research Institute (SwRI) has unveiled a significantly improved model for mapping the Sun’s entire surface, including the hemisphere that is permanently hidden from direct view from Earth. The new framework, described in a paper published this month in The Astrophysical Journal Supplement Series, incorporates activity on the far side of the Sun into surface flux transport models with unprecedented accuracy, and it could sharpen the space weather forecasts on which satellite operators, power grid managers, and astronauts increasingly depend.

Surface flux transport, or SFT, models are among the most important tools solar physicists use to monitor and forecast the behavior of the Sun. These models track how magnetic flux emerges on the solar surface and how it is subsequently carried around by large-scale flows, including the differential rotation of the Sun and the meridional circulation that transports magnetic material toward the poles. By simulating the evolution of the surface magnetic field over weeks, months, and years, SFT models allow researchers to understand how solar activity builds up and decays across the roughly eleven-year solar cycle. That understanding, in turn, feeds into predictions of sunspots, flares, and coronal mass ejections, the explosive events that can send bursts of radiation and charged particles toward Earth and disrupt communications, navigation systems, and electrical infrastructure.

The central challenge the SwRI-led team set out to solve is a fundamental observational one: at any given moment, half of the Sun is facing away from Earth. Unlike the Moon, which is tidally locked and shows us the same face forever, the Sun rotates completely on its axis roughly every 27 days, so every patch of the solar surface eventually swings into view. But between those appearances, active regions can form, evolve, and decay entirely out of sight. Earlier SFT models, lacking simultaneous data from the hidden hemisphere, were often caught off guard. In some cases, as the Sun rotated, a sunspot would emerge unexpectedly from the far side, forcing researchers into quick recalculations of their magnetic maps and degrading the reliability of forecasts built on them.

To fill this observational gap, scientists have since the 1970s turned to helioseismology, a technique that studies the Sun’s interior and its far side using acoustic waves. Just as geologists probe what lies deep beneath the Earth’s crust by analyzing how seismic waves travel through the planet, solar physicists analyze sound waves reverberating through the Sun’s interior. Active regions on the far side perturb these acoustic signals in characteristic ways, allowing researchers to infer the presence of magnetically active areas before they rotate into view. Helioseismic far-side imaging has led to vast improvements in space weather forecasting, giving forecasters days of advance warning about emerging sunspot groups that would otherwise appear without notice.

Yet even helioseismic models have been hampered by bad information. The acoustic signatures that betray an active region are not always what they seem. Dr. Bibhuti Jha, a research scientist in SwRI’s Solar System Science and Exploration Division in Boulder, Colorado, and the lead author of the new paper, described the problem in vivid terms. Acoustic data can sometimes indicate an active region on the far side, he explained, but when the area finally comes into view, nothing is there. It is almost like a false positive, and the team calls these phantom features ghost active regions. When such false detections are folded into an SFT model, they inject spurious magnetic flux that then propagates through the simulation, contaminating the map of the solar surface and any forecasts derived from it.

The new framework tackles this problem directly by introducing a probabilistic treatment of far-side detections. Rather than accepting every helioseismic detection at face value, the model systematically evaluates the accuracy of each candidate active region before it is incorporated into the surface flux transport simulation. Dr. Lisa Upton, an SwRI lead scientist also based in the Boulder office and a co-author of the paper, explained the approach: before far-side helioseismology data are added to the model, the team associates a probability with each far-side active region based on its properties. Detections with characteristics that closely match confirmed active regions earn high confidence, while ambiguous or weak signals are weighted accordingly or filtered out. The result is a model that weeds out ghost active regions and delivers a more accurate map of the entire solar surface at any given time.

To build and validate this framework, the team studied near-simultaneous maps of the whole solar surface from 2010 to 2014, a period that spans the rise to the maximum of Solar Cycle 24. During this window, observations from Earth-based instruments could be combined with data from space-based assets positioned to view the opposite hemisphere, providing ground truth against which far-side helioseismic detections could be checked. By comparing what the acoustic methods predicted with what was actually observed when each region rotated into view, the researchers could quantify the reliability of far-side detections and calibrate their probabilistic assignments. The resulting SFT models are more complete and accurate than any previous effort, according to the team.

The significance of this work extends well beyond the technical details of magnetic cartography. Solar activity drives space weather, and space weather is increasingly recognized as a practical hazard with economic and security implications. Strong solar flares can degrade high-frequency radio communications used by aviation and maritime operations, while coronal mass ejections can induce currents in long power lines and damage transformers, as demonstrated by historic geomagnetic storms. Satellites in Earth orbit face elevated radiation risks during active periods, and crewed missions beyond low Earth orbit require accurate forecasts to protect astronauts from energetic particle events. A more faithful representation of the Sun’s global magnetic field, including the half that cannot be seen directly, gives forecasters a firmer foundation for anticipating when and where the next eruption might occur.

The probabilistic framework also provides a practical pathway for incorporating helioseismic data into SFT models more broadly, something that has been difficult to do rigorously in the past because of the noise and false positives inherent in far-side imaging. By treating each detection as a probability rather than a certainty, the method allows models to benefit from far-side information without being misled by its errors, a balance that previous approaches struggled to strike. Jha characterized the advance as a step toward a more realistic modeling of the magnetic field of both the visible and invisible hemispheres of the Sun, a goal that has motivated solar physicists for decades.

Looking ahead, the team hopes that better surface maps will translate into better predictions of the solar cycle itself. As Jha noted, modeling the surface of the Sun to see how the magnetic field behaves helps scientists understand how solar activity varies over the cycle, and if the next solar cycle can be plotted out in advance, researchers will have a much better idea of how the Sun is going to behave in the coming years. The study, titled A Probabilistic Framework for Incorporating Helioseismic Far-Side Active Regions into SFT Models, was published on October 8, 2026, in The Astrophysical Journal Supplement Series, and it arrives at a moment of growing public and governmental attention to space weather readiness. As society’s dependence on technology vulnerable to solar storms continues to grow, tools that make the Sun’s hidden half visible, even probabilistically, represent a meaningful stride toward a more predictable star.

Subject of Research: Probabilistic incorporation of helioseismic far-side active region data into solar surface flux transport models

Article Title: SwRI-led team develops improved model for mapping the Sun

Article References: SwRI-led team develops improved model for mapping the Sun. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Sun, solar magnetic field, surface flux transport, helioseismology, far-side imaging, space weather, solar cycle, sunspots, SwRI, solar forecasting, active regions, The Astrophysical Journal Supplement Series