Artificial Intelligence at the Edge of the Sun

Here’s the sun, restless and brilliant, spitting storms that can scramble GPS, blind satellites, and nudge power grids toward the edge. For decades, space-weather forecasters have watched and modeled its moods. Now NASA is adding a new co-pilot: AI.

In mid-2025 NASA and IBM unveiled Surya, a heliophysics foundation model trained on years of high-resolution observations of the Sun. Surya ingests extreme-ultraviolet imagery and other solar data to anticipate outbursts—flares and other phenomena that can trigger chain reactions from the upper atmosphere to the ground. NASA says models like this can give earlier warnings to satellite operators and help predict how changes in the Sun’s ultraviolet output ripple through Earth’s ionosphere—crucial for communications and navigation.

The bigger story is that Surya isn’t a one-off gadget; it’s part of a strategy to fuse physics-based models with machine learning. NASA’s own overview of recent heliophysics work highlights teams that pair coronagraph and heliospheric images with ML classifiers to judge whether a coronal mass ejection (CME) will be “geoeffective”—that is, actually disturb Earth’s magnetic field when it arrives. One approach, GeoCME, is emblematic: learn from past events to flag the ones most likely to cause trouble.

AI is also being pushed right to the operational front line. In 2023, NASA described an ML-powered system that acts like a tornado siren for space weather, combining satellite data with AI to forecast hazardous conditions that threaten technology and power infrastructure. The goal isn’t to replace human experts but to buy precious lead time and triage attention on the events most likely to matter.

A lot of this acceleration has come from rapid-prototyping programs at the Frontier Development Lab (FDL)—an applied-AI accelerator run with NASA partners. FDL’s Heliolab teams have tested deep-learning pipelines on EUV imagery to assess whether solar events are eruptive, and to build “virtual instruments” that can plug into foundation models like Surya. It’s an R&D relay race: build on open data, iterate with ML, and fold the best pieces back into NASA’s toolchain.

Surya itself has been open-sourced with IBM, signaling NASA’s intent to make heliophysics AI a community endeavor. IBM characterizes Surya as the first AI foundation model in this domain, designed to help predict solar weather that endangers astronauts, satellites, and terrestrial systems—and to do it faster than current methods. Opening the weights and code invites labs worldwide to fine-tune for niche tasks (say, flare nowcasting versus CME tracking) and to benchmark against shared datasets.

Meanwhile, peer-reviewed research shows why AI is so attractive here: the Sun is periodic, but not politely so. Long-short term memory (LSTM) and related architectures have proven adept at learning solar cycles and flare statistics from decades of time series, improving long-range forecasts of sunspot numbers and related indices that underpin everything from satellite-drag models to HF radio planning. That research gives NASA and partners a menu of architectures to test as they plug AI into the space-weather stack.

Why this matters now

Solar Cycle 25 has already delivered the strongest storms in years, and the next big disturbances won’t wait for cleanroom schedules. By pairing physics with pattern recognition, NASA aims to:

• Warn earlier. Minutes to hours of extra lead time help operators reorient satellites, schedule instrument safe modes, and protect astronauts.

• Target the right threats. ML classifiers help sort “spectacular but harmless” solar fireworks from CMEs that will actually couple with Earth’s magnetosphere.

• Share tools openly. Foundation models and FDL prototypes seed a broader ecosystem, so forecasting improves everywhere—academia, industry, and national agencies.

The endgame is pragmatic: fewer surprises. Better forecasts mean steadier GPS and comms, fewer satellite anomalies, and power grids that can brace before currents surge. The Sun will keep throwing curveballs; NASA’s bet is that AI can teach us to read the pitcher’s hand a little sooner.

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Sources

1. NASA Science: “NASA, IBM’s ‘Hot’ New AI Model Unlocks Secrets of Sun” (Aug. 2025). NASA Science

2. NASA Science: “NASA Missions Help Explain, Predict Severity of Solar Storms” (July 1, 2025) — includes machine-learning GeoCME approach. NASA Science

3. NASA (Mar. 30, 2023): “NASA-enabled AI Predictions May Give Time to Prepare for Solar Storms.” NASA

4. IBM Research Blog (Aug. 20, 2025): “Introducing Surya, a new heliophysics foundation model.” IBM Research

5. Frontiers in Astronomy and Space Sciences (2025): “Forecasting long-term sunspot numbers using the LSTM-WGAN model.” Frontiers

Eyes On The Skies – Space Weather and Satellites

The Space Surveillance Network  has been tracking space objects since 1957 when the Soviets opened the space age with the launch of Sputnik I. Since then, the SSN has tracked more than 24,500 space objects orbiting Earth.

Of that number, the SSN currently tracks more than 8,000 orbiting objects.

The rest have re-entered Earth’s turbulent atmosphere and disintegrated, or survived re-enty and impacted the Earth. The space objects now orbiting Earth range from satellites weighting several tons to pieces of spent rocket bodies weighing only 10 pounds.

About seven percent of the space objects are operational satellites, the rest are debris. USSPACECOM is primarily interested in the active satellites, but also tracks space debris. The SSN tracks space objects which are 10 centimeters in diameter (baseball size) or larger.

SSN Sensors

The SSN uses a “predictive” technique to monitor space objects; it spot checks them rather than tracking them continually. This technique is used because of the limits of the SSN (number of sensors, geographic distribution, capability, and availability).

Phased-array radars can maintain tracks on multiple satellites simultaneously and scan large areas of space in a fraction of a second. These radar’s have no moving mechanical parts to limit the speed of the radar scan – the radar energy is steered electronically.

Tracking and monitoring things like space weather and debris can help prevent issues, and can even keep our military satellites safe from storms and debris.  Dr. Alex Young, Solar Physicist at the NASA Goddard Space Flight Center , explains how space weather could impact our military satellites, and how monitoring it – and developing new technology to understand it – can help the mission.

Conventional radars use immobile detection and tracking antennas. The detection antenna transmits radar energy into space in the shape of a large fan. When a satellite intersects the fan, the energy is reflected back to the antenna, triggering the tracking antenna.

The tracking antenna locks its narrow beam of energy on the target and follows it in order to establish orbital data.

The Ground-Based Electro-Optical Deep Space Surveillance System (GEODSS) consists of three telescope sensors linked to a video camera. The video cameras feed their space pictures into a nearby computer which drives a display scope. The image is transposed into electrical impulses and recorded on magnetic tape. This is the same process used by video cameras. Thus, the image can be recorded and analyzed in real-time.

Combined, these types of sensors make up to 80,000 satellite observations each day.

This enormous amount of data comes from SSN sites such as Maui, Hawaii; Eglin, Florida; Thule, Greenland; and Diego Garcia, Indian Ocean. The data is transmitted directly to USSPACECOM ‘s Space Control Center (SCC) via satellite, ground wire, microwave and phone. Every available means of communications is used to ensure a backup is readily available if necessary.

Information for this post provided by the U.S. Air Force Space Surveillance Network 

Space Weather, Satellites and the Sun

When service members go out on patrol, they keep a weather eye out for any dangers or unknown variables that might impact the mission.  When space surveillance specialists go out on the job, they’re keeping an eye on the skies, and in more ways than one.

Space surveillance is a critical part of USSPACECOM‘s mission and involves detecting, tracking, cataloging and identifying man-made objects orbiting Earth, i.e. active/inactive satellites, spent rocket bodies, or fragmentation debris.

Space surveillance can predict when and where a decaying space object will re-enter the Earth’s atmosphere and prevent a returning space object.  To radar, these can look like a missile, and even trigger a false alarm from missile-attack warning sensors of the U.S. and other countries.

Therefore, it’s important that we monitor the skies as much as we monitor anything that impacts us as a nation, and in this case, as a planet.

Space surveillance can also chart the present position of space objects and plot their anticipated orbital paths.  This means detecting new man-made objects in space, producing a running catalog of man-made space objects, determining which country owns a re-entering space object, and  informing NASA whether or not objects may interfere with the space shuttle and Russian Mir space station orbits.

The command accomplishes these tasks through its Space Surveillance Network (SSN) of U.S. Army, Navy and Air Force operated, ground-based radar’s and optical sensors at 25 sites worldwide.

One of the things that affects our satellites – and something we have to be cognizant of – is space weather, and specifically, solar weather.  Dr. Alex Young, Solar Physicist at the NASA Goddard Space Flight Center, explains how the sun is making scientific waves in our daily lives.