Tucson Standard

University of Arizona researchers have developed a groundbreaking spectral atlas, akin to a fingerprint database, for satellites in geostationary orbit. This initiative marks an essential step towards the identification of human-made objects in space.

The atlas encompasses 96 satellites visible from Tucson and serves as a baseline for differentiating space objects. Adam Battle, a Ph.D. candidate at the University of Arizona's Lunar and Planetary Laboratory and supported by the Space4 Center, spearheaded this research. The findings were published in The Planetary Science Journal.

"Satellites look like tiny dots in the sky, even through most telescopes. For decades, humans have launched tens of thousands of objects into space with no mechanism for identifying them easily," Battle said. "This is the first time this sort of systematic, big atlas of spectral data has been collected for these objects."

The United States Space Surveillance Network monitors over 45,000 artificial objects orbiting Earth, including approximately 350 active payloads in geostationary orbit (GEO). GEO is about 22,000 miles above Earth's equator and holds satellites that appear stationary from the ground due to their synchronized orbital speed with Earth.

"The orbital space around the Earth is getting congested, and we unfortunately do not have license plates for satellites to identify them easily. This work is the first step towards making space safe, secure and sustainable," stated Vishnu Reddy, director of the university's Space4 Center.

The research team utilized spectroscopy to analyze how sunlight interacts with satellite materials such as metals and solar panels under various illumination conditions. These interactions produce distinct color patterns or "fingerprints" unique to each satellite.

Observations were conducted using a student-built telescope on campus over 192 nights between January 2020 and June 2022. This effort generated 284 datasets and around 190,000 spectra.

"The next step is to combine the power of machine learning with this incredible dataset for fast and credible identification of satellites," said Roberto Furfaro, co-author and professor at U of A.

David Cantillo plans to extend this research by building a telescope in Australia to study GEO objects not visible from the U.S., aiming for broader applications in space object identification.