ExoMiner discovery of ExoPlanets via data from the Kepler and TESS space telescopes
Description
ExoMiner is being used to statistically validate exoplanets detected by the Kepler space telescope and to identify promising exoplanet candidates for the TESS space telescope
Detailed example
ExoMiner produces scores between 0.0 and 1.0 for a number of possible categories relevant to transiting exoplanet searches.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
ExoMiner has been used to validated 370 new exoplanets identified in data from the Kepler space telescope. It is now being used to idenitify promising exoplanet candidates in TESS mission data. It promises to significantly reduce the manual effort required while improving the accuracy of identify promising TESS exoplanet candidates and to reject astrophysical false positives and instrumental false alarms.
Controls / human review
ATO: Not reported; PIA: Not published