Deep Learning Vetting of TESS FFI Data: Results and Comparison with 2-Min Data
We present the results of vetting TCEs from the TESS SPOC full-frame images (FFI) Year 5 data using our deep learning model, and we compare the performance in this dataset against the results obtained for the TESS SPOC 2-min data. The 200-second cadence FFI data expands the search to a list of targets that not only includes 2-minute targets, but also potentially high-value targets within 100 parsecs or with H-magnitude <10, and field targets with TESS magnitude <13.5. This work aims to explore this rich dataset and increase the efficiency and throughput of the vetting process by helping unearth more high-quality planet candidates from the TESS mission.