Part 6/8:
This strategic integration of machine learning has been co-signed by Dr. Theo Dulas, who emphasized the efficiency of the algorithm. “Once built and trained, the algorithm is faster than existing techniques and can be completely automated,” he noted, making it an ideal fit for analyzing large volumes of planetary candidates identified in current surveys like TESS.
The Future of Planet Discovery
The team argues that employing this machine learning approach in conjunction with traditional methods is vital, particularly as nearly 30% of known planets to date have only been validated through singular methods. Developing new validation techniques not only enhances the existing methodology but also enables astronomers to swiftly prioritize candidates for further research.