NASA AI Model That Found 370 Exoplanets Now Digs Into TESS Data

The Power of Open-Source Science: How NASA's ExoMiner++ is Revolutionizing Exoplanet Discovery

In the quest to explore the vast expanse of our universe, scientists have made tremendous strides in discovering exoplanets, worlds that orbit stars other than our Sun. Over 6,000 planets have been discovered so far, with more than half of these discoveries attributed to data from NASA's retired Kepler mission and the current TESS (Transiting Exoplanet Survey Satellite) mission. However, the sheer volume of data from these missions still holds many secrets, and researchers are using artificial intelligence (AI) to uncover them.

The Birth of ExoMiner++

In 2021, a team from NASA's Ames Research Center in California's Silicon Valley created ExoMiner, a piece of open-source software that used AI to validate 370 new exoplanets from Kepler data. The team, led by Hamed Valizadegan, a KBR employee at NASA Ames, has now created a new version of the model, ExoMiner++, which is trained on both Kepler and TESS data. This updated algorithm has already identified 7,000 targets as exoplanet candidates from TESS data on an initial run.

How ExoMiner++ Works

ExoMiner++ uses a deep learning approach to sift through observations of possible transits to predict which ones are caused by exoplanets and which ones are caused by other astronomical events, such as eclipsing binary stars. The model is designed to handle the enormous volume of data from TESS, which surveys nearly the whole sky, mainly looking for planets transiting nearby stars. By combining data from both Kepler and TESS, ExoMiner++ can deliver strong results, even with limited resources.

The Importance of Open-Source Science

The open-source nature of ExoMiner++ is a key factor in its success. By making the software freely available, researchers can replicate the results and dig deeper into the data, which is why open data and code are essential pillars of gold-standard science. As Kevin Murphy, NASA's chief science data officer, notes, "Open-source software like ExoMiner accelerates scientific discovery."

Future Implications

The advances made with ExoMiner++ will have significant implications for future exoplanet-hunting missions. NASA's upcoming Nancy Grace Roman Space Telescope will capture tens of thousands of exoplanet transits, and ExoMiner++ will be able to analyze this data to identify new exoplanet candidates. The open science initiative out of NASA will lead to not just better science, but also better software, as Jon Jenkins, an exoplanet scientist at NASA Ames, notes, "Open-source science and open-source software are why the exoplanet field is advancing as quickly as it is."

Conclusion

The discovery of exoplanets is a rapidly evolving field, and the use of AI and open-source science is revolutionizing the way researchers approach this challenge. ExoMiner++ is a powerful tool that has already identified thousands of new exoplanet candidates and will continue to play a key role in the discovery of new worlds. As we look to the future, it is clear that the combination of AI, open-source science, and collaborative research will continue to drive breakthroughs in our understanding of the universe.

Related Terms

• Open Science: The practice of making scientific data, tools, research, and software freely available to the public.
• Ames Research Center: A NASA research center located in California's Silicon Valley, known for its work in space exploration and technology development.
• Artificial Intelligence (AI): A field of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence.
• Exoplanets: Planets that orbit stars other than our Sun.
• Kepler / K2: A NASA space telescope that was used to discover thousands of exoplanets.
• TESS (Transiting Exoplanet Survey Satellite): A NASA space telescope that is used to discover exoplanets by detecting the dimming of starlight as a planet passes in front of its host star.

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Source: https://science.nasa.gov/open-science/deep-learning-exoplanets-tess/