Anne’s Story: From student to planet hunter
Google・2 minutes read
A neural network developed by Chris and Professor Andrew Vanderburgh analyzed data from the broken reaction wheels of NASA's Kepler space telescope, leading to the discovery of new planets through machine learning despite initial challenges. This success story showcases the evolution from a novice to a proficient planet hunter.
Insights
- Machine learning, specifically a neural network developed by Chris and Professor Andrew Vanderburgh, played a pivotal role in discovering new planets after NASA's Kepler space telescope's reaction wheels malfunctioned in 2013.
- The successful implementation of machine learning in analyzing data from the Kepler telescope highlights the transformative power of technology in scientific exploration, showcasing how innovation and adaptation can lead to groundbreaking discoveries in astronomy.
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Recent questions
How did NASA discover new planets in 2013?
In 2013, NASA discovered new planets by developing a neural network to analyze data from the Kepler space telescope. This network was created by Chris and Professor Andrew Vanderburgh after Kepler's reaction wheels broke. By observing brightness dips when objects passed in front of stars, the network utilized machine learning to identify new planets, showcasing the transition from a beginner to a successful planet hunter.
What was the purpose of the Kepler space telescope?
The Kepler space telescope, launched by NASA in 2009, aimed to find planets by observing brightness dips when objects passed in front of stars. This telescope was instrumental in discovering new planets and expanding our understanding of the universe.
Who developed the neural network for analyzing Kepler's data?
The neural network for analyzing Kepler's data was developed by Chris and Professor Andrew Vanderburgh. After Kepler's reaction wheels broke in 2013, they created this network to process the telescope's data and identify new planets through machine learning.
How did the neural network contribute to planet discovery?
The neural network developed by Chris and Professor Andrew Vanderburgh played a crucial role in discovering new planets. By analyzing data from the Kepler space telescope, the network utilized machine learning to identify brightness dips when objects passed in front of stars, leading to the discovery of new planets and showcasing the power of artificial intelligence in space exploration.
What breakthrough did the neural network achieve in planet hunting?
The neural network developed for analyzing Kepler's data achieved a breakthrough in planet hunting by utilizing machine learning to identify new planets. Despite initial struggles, modifications to the network allowed for the successful discovery of planets, highlighting the importance of innovation and adaptation in scientific research.
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Summary
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Kepler's Neural Network: From Struggle to Success
- In 2009, NASA launched the Kepler space telescope to find planets by observing brightness dips when something passes in front of stars. When Kepler's reaction wheels broke in 2013, a neural network was developed by Chris and Professor Andrew Vanderburgh to analyze the data. Despite initial struggles, the network was modified, leading to the discovery of new planets through machine learning, showcasing the journey from a beginner to a successful planet hunter.
