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The Transiting Exoplanet Survey Satellite (TESS) has now been operational fora little over two years, covering the Northern and the Southern hemispheresonce. The TESS team processes the downlinked data using the Science ProcessingOperations Center pipeline and Quick Look pipeline to generate alerts forfollow-up. Combined with other efforts from the community, over two thousandplanet candidates have been found of which tens have been confirmed as planets.We present our pipeline, Nigraha, that is complementary to these approaches.Nigraha uses a combination of transit finding, supervised machine learning, anddetailed vetting to identify with high confidence a few planet candidates thatwere missed by prior searches. In particular, we identify high signal to noiseratio (SNR) shallow transits that may represent more Earth-like planets. In thespirit of open data exploration we provide details of our pipeline, release oursupervised machine learning model and code as open source, and make public the38 candidates we have found in seven sectors. The model can easily be run onother sectors as is. As part of future work we outline ways to increase theyield by strengthening some of the steps where we have been conservative anddiscarded objects for lack of a datum or two.

Nigraha: Machine-learning based pipeline to identify and evaluate planet candidates from TESS