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Mapping Dark Matter
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Aerospace
- Software
Applicable Functions
- Product Research & Development
Use Cases
- Digital Twin
Services
- Data Science Services
- Software Design & Engineering Services
The Challenge
The universe is filled with 'dark matter'—invisible, heavy matter that distorts light as it travels from distant galaxies. To create an accurate map of the universe, scientists must account for the way dark matter distorts our images of space. NASA, the British Royal Astronomical Society, and the European Space Agency sponsored the Mapping Dark Matter research competition to solve this problem. Participants were given 100,000 galaxy images, blurred to simulate the effects of dark matter. They had three months to create models to find the real shapes of galaxies; their results were scored for accuracy against known measurements.
About The Customer
The competition was sponsored by NASA, the British Royal Astronomical Society, and the European Space Agency. These organizations are at the forefront of astronomical research and space exploration. NASA, the United States' space agency, is known for its pioneering work in space missions and scientific research. The British Royal Astronomical Society is a learned society that promotes the study of astronomy, solar-system science, geophysics, and closely related branches of science. The European Space Agency is an intergovernmental organization dedicated to the exploration of space, with 22 member states. The competition attracted 72 teams from diverse fields, including handwriting recognition and string theory, highlighting the interdisciplinary nature of the challenge.
The Solution
Within the first week of the competition, Martin O’Leary, a British glaciologist, had created a solution so advanced that the White House Blog announced he had 'outperformed the state-of-the-art algorithms most commonly used in astronomy.' Meanwhile, David Kirkby and Daniel Margala, cosmologists at UC Irvine, developed an artificial neural network to recognize patterns in the galaxy images. The competition saw participation from 72 teams, including experts from fields as diverse as handwriting recognition and string theory. The winning team produced a 3x increase in accuracy over the state-of-the-art benchmark that had taken NASA decades to develop. The winners were awarded a trip to present their methods to NASA and other agencies.
Operational Impact
Quantitative Benefit
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