Date:
Tue, 09/12/202512:30-13:30
Speaker: Oryna Ivashtenko (Faculty of Physics, WIS)
Title:Reliable detection of small long-period planets in Kepler data
Abstract:The Kepler spacecraft provided unprecedented photometric precision, enabling the detection of Earth-like planets. However, the final Kepler catalog lacks sufficient reliability for small, long-period planets, complicating estimates of occurrence rates in this regime. A major source of this problem is the difficulty in distinguishing genuine but faint planetary signals from systematic false alarms—spurious detections caused by correlated, non-Gaussian noise.We developed an independent search and vetting pipeline that addresses this noise structure using tailored statistical methods, providing a clean background distribution free from the false alarm tail. The pipeline was applied to the entire Kepler dataset, yielding a list of planetary candidates. For each candidate, we estimated its probability of being a real astrophysical periodic signal, using empirical per-target background estimation and injection-recovery campaign.In this talk, I will outline the methodology behind this pipeline and describe how it will contribute to robust occurrence rate estimates in our upcoming work.
Zoom: https://huji.zoom.us/j/84347178812?pwd=O5pq5eIDVgQe2PYjb1wWDAeChFbM3r.1
Meeting ID: 843 4717 8812
Passcode: 575367
Contact: Zhiyuan Yao
Title:Reliable detection of small long-period planets in Kepler data
Abstract:The Kepler spacecraft provided unprecedented photometric precision, enabling the detection of Earth-like planets. However, the final Kepler catalog lacks sufficient reliability for small, long-period planets, complicating estimates of occurrence rates in this regime. A major source of this problem is the difficulty in distinguishing genuine but faint planetary signals from systematic false alarms—spurious detections caused by correlated, non-Gaussian noise.We developed an independent search and vetting pipeline that addresses this noise structure using tailored statistical methods, providing a clean background distribution free from the false alarm tail. The pipeline was applied to the entire Kepler dataset, yielding a list of planetary candidates. For each candidate, we estimated its probability of being a real astrophysical periodic signal, using empirical per-target background estimation and injection-recovery campaign.In this talk, I will outline the methodology behind this pipeline and describe how it will contribute to robust occurrence rate estimates in our upcoming work.
Zoom: https://huji.zoom.us/j/84347178812?pwd=O5pq5eIDVgQe2PYjb1wWDAeChFbM3r.1
Meeting ID: 843 4717 8812
Passcode: 575367
Contact: Zhiyuan Yao