  A novel artificial intelligence (AI) technique that uses machine learning
(ML) methodologies combines several algorithms, which were developed by
ThetaRay, Inc., is applied to NASA's Transiting Exoplanets Survey Satellite
(TESS) dataset to identify exoplanetary candidates. The AI/ML ThetaRay system
is trained initially with Kepler exoplanetary data and validated with confirmed
exoplanets before its application to TESS data. Existing and new features of
the data, based on various observational parameters, are constructed and used
in the AI/ML analysis by employing semi-supervised and unsupervised machine
learning techniques. By the application of ThetaRay system to 10,803 light
curves of threshold crossing events (TCEs) produced by the TESS mission,
obtained from the Mikulski Archive for Space Telescopes, we uncover 39 new
exoplanetary candidates (EPC) targets. This study demonstrates for the first
time the successful application of combined multiple AI/ML-based methodologies
to a large astrophysical dataset for rapid automated classification of EPCs.
