Key points are not available for this paper at this time.
Abstract Nearly one million light curves from the TESS Year 1 southern hemisphere extracted from Full Field Images with the DIAmante pipeline are processed through the AutoRegressive Planet Search statistical procedure. ARIMA models remove lingering autocorrelated noise, the Transit Comb Filter identifies the strongest periodic signal in the light curve, and a Random Forest machine-learning classifier is trained and applied to identify the best potential candidates. Classifier training sets are based on injections of planetary transit signals, eclipsing binaries, and other variable stars. The optimized classifier has a True Positive Rate of 92.5% and a False Positive Rate of 0.43% from the labeled training set. The result of this DIAmante TESS autoregressive planet search of the southern ecliptic hemisphere analysis is a list of 7377 potential exoplanet candidates. The classifier had a 64% recall rate for previously confirmed exoplanets and a 78% negative recall rate for known False Positives. The completeness map of the injected planetary signals shows high recall rates for planets with 8–30 R ⊕ radii and periods 0.6–13 days and poor completeness for planets with radii <2 R ⊕ or periods <1 day. The list has many False Alarms and False Positives that need to be culled with multifaceted vetting operations (Paper II).
Melton et al. (Tue,) studied this question.