A groundbreaking algorithm developed by a Georgia Tech Ph.D. student is transforming how scientists study the surface of Mars, with far-reaching implications for understanding extreme weather events on Earth.
Nested Fusion: Decoding Mars
This new algorithm, Nested Fusion, is the brainchild of Austin P. Wright, Ph. D. student at Georgia Tech. The Nested Fusion process merges data from the Perseverance Rover’s instruments, including the Planetary Instrument for X-ray Lithochemistry (PIXL), to offer a holistic view of areas on Mars.
The PIIN of the X-ray Fluorescence (XRF) Spectrometer and Multi-Context Camera (MCC), which are the two primary instruments of PIXL, can work together to collect complementary data about the elemental composition and physical properties of Martian samples. However, the differing resolutions of those instruments have been conventionally difficult to map onto a common data layer.
Enter Nested Fusion (NF for short). The combination of these datasets allows the algorithm to produce a single large, high-resolution image distribution such that NASA scientist counterparts can more readily interpret multiple satellite data studies at once. These findings can speed up the hunt for past life on Mars as well as help us learn about the geology of the planet.
Commercial Uses: Weather Forecast for Extreme Weather Earthly Applications.
The implications of Nested Fusion reach beyond the depths of Mars’s crust. Wright and his colleagues believe this algorithm could change how we analyze data in many scientific fields here on Earth as well.
Kasra recognized the first applications reside in predicting catastrophic weather events as well as hurricanes, wildfires and other natural disasters affecting millions worldwide. Nested Fusion is a technique designed to process overlapping datasets from satellite imagery, biomarkers, and climate data so that coregistration can assist epidemiologists in understanding the complicated patterns and relationships for these extreme weather phenomena.
Historically, cross-correlational analysis of these huge datasets has been a slow process that can postpone the discovery of important insights. However, Nested Fusion allows researchers to identify these firm substructures far sooner, ultimately allowing a much more accurate and timely model for prediction. That could dramatically improve our ability to prepare for and minimize the impact of these extreme weather events, which cost lives as well as feelings on an economic and social level.
Conclusion
The creation of the Nested Fusion algorithm by Austin P. Wright et al at Georgia Tech is a great example of how data science can change the game in traditional domains of scientific research. By connecting the dots in complicated datasets and easing the minds of data scientists who sort through them, Nested Fusion could be poised to unearth new insights on Mars — and back here at home. With the further refinement and application of this algorithm to different fields, it has the promise to be profoundly important in our understanding of nature.
