Mapping Dark Matter
公司规模
1,000+
地区
- America
- Europe
国家
- United Kingdom
- United States
产品
- Euclid Space Telescope
技术栈
- Artificial Neural Networks
实施规模
- Enterprise-wide Deployment
影响指标
- Digital Expertise
- Innovation Output
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 航天
- Software
适用功能
- 产品研发
用例
- 数字孪生
服务
- 数据科学服务
- 软件设计与工程服务
关于客户
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.
挑战
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.
解决方案
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.
运营影响
数量效益
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