Probing ExoMiner for Effectiveness against False Alarms in Kepler Data

We present a study on the effectiveness of ExoMiner against False Alarms in Kepler data. ExoMiner is a deep learning model that was used to validate around 370 Kepler Objects of Interest. We follow the analysis conducted in Coughlin et al (2017) “DR25 Robovetter Completeness and Effectiveness” for Robovetter, a rule-based model used to vet TCEs for this data release and automatically generate the Q1-Q17 DR 25 KOI Table. The ExoMiner model is trained on observed transit data from Kepler Q1-Q17 DR25 and evaluated on Kepler inverted and scrambled data. The results provide a more comprehensive insight into the capacities and limitations of ExoMiner, especially the vetting of not-transit-like signals and, more generally, the use of deep learning models to model transit photometry data for vetting and validation purposes.