Part 5/8:
A New Era of Planetary Validation Techniques
Armstrong highlighted the significant distinction of their machine learning approach in the context of planetary validation. Instead of merely ranking the likelihood of candidates being actual planets, this new probabilistic framework quantifies and establishes statistical likelihoods with a precision previously unattainable. By using this method, when a candidate's probability of being a false positive falls below 1%, it can be confidently classified as a validated planet.