New AI Model Unveils 7,000 Exoplanet Candidates from TESS Data
NASA has announced the launch of a new artificial intelligence (AI) model, ExoMiner++, which can identify thousands of potential exoplanets in data collected by its Transiting Exoplanet Survey Satellite (TESS). The model was trained on both TESS and Kepler mission data, producing an impressive 7,000 exoplanet candidates.
The latest version of the model uses deep learning technologies to sift through vast amounts of observational data and predict which signals are likely to be caused by planets. This approach allows for a more efficient use of resources than traditional methods, as mentioned by Hamed Valizadegan, the project lead for ExoMiner.
ExoMiner++ is an open-source software package developed by NASA scientists at the Ames Research Center in California. The new model is designed to be used by researchers from around the world, who can freely download and use it to hunt for planets in TESS data. This approach aligns with NASA's commitment to transparent and reproducible scientific research.
The team behind ExoMiner++, led by Jon Jenkins, an exoplanet scientist at NASA Ames, emphasizes the importance of open science initiatives in accelerating scientific discovery. They believe that the sharing of data, tools, research, and software maximizes the impact of NASA's science missions.
As future exoplanet-hunting missions like NASA's Nancy Grace Roman Space Telescope generate more data, the advances made with ExoMiner models could also help hunt for exoplanets in Roman data. The open science initiative out of NASA aims to lead to better science and better software, as stated by Jenkins.
This new AI model marks another significant step forward in the discovery of exoplanets, which are celestial bodies that orbit stars other than our Sun. With thousands of potential exoplanets identified from TESS data, scientists can begin exploring these newly discovered worlds and uncovering secrets about their composition and properties.
NASA has announced the launch of a new artificial intelligence (AI) model, ExoMiner++, which can identify thousands of potential exoplanets in data collected by its Transiting Exoplanet Survey Satellite (TESS). The model was trained on both TESS and Kepler mission data, producing an impressive 7,000 exoplanet candidates.
The latest version of the model uses deep learning technologies to sift through vast amounts of observational data and predict which signals are likely to be caused by planets. This approach allows for a more efficient use of resources than traditional methods, as mentioned by Hamed Valizadegan, the project lead for ExoMiner.
ExoMiner++ is an open-source software package developed by NASA scientists at the Ames Research Center in California. The new model is designed to be used by researchers from around the world, who can freely download and use it to hunt for planets in TESS data. This approach aligns with NASA's commitment to transparent and reproducible scientific research.
The team behind ExoMiner++, led by Jon Jenkins, an exoplanet scientist at NASA Ames, emphasizes the importance of open science initiatives in accelerating scientific discovery. They believe that the sharing of data, tools, research, and software maximizes the impact of NASA's science missions.
As future exoplanet-hunting missions like NASA's Nancy Grace Roman Space Telescope generate more data, the advances made with ExoMiner models could also help hunt for exoplanets in Roman data. The open science initiative out of NASA aims to lead to better science and better software, as stated by Jenkins.
This new AI model marks another significant step forward in the discovery of exoplanets, which are celestial bodies that orbit stars other than our Sun. With thousands of potential exoplanets identified from TESS data, scientists can begin exploring these newly discovered worlds and uncovering secrets about their composition and properties.