Speaker
Kaiming Cui
(University of Warwick)
Description
Understanding the demographics of close-in Neptune-sized planets is a key to exploring planet formation, particularly around the Neptunian desert. We performed a comprehensive search of TESS SPOC Full Frame Image light curves to identify transit signals. Candidate validation was conducted with our RAVEN pipeline, utilizing machine learning to identify false positives. The resulting sample enabled a robust statistical occurrence rate estimation for close-in Neptunes orbiting FGK stars, offering critical insights into their distribution and the processes shaping planetary system architectures.
