  The Transiting Exoplanet Survey Satellite (TESS) has now been operational for
a little over two years, covering the Northern and the Southern hemispheres
once. The TESS team processes the downlinked data using the Science Processing
Operations Center pipeline and Quick Look pipeline to generate alerts for
follow-up. Combined with other efforts from the community, over two thousand
planet 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, and
detailed vetting to identify with high confidence a few planet candidates that
were missed by prior searches. In particular, we identify high signal to noise
ratio (SNR) shallow transits that may represent more Earth-like planets. In the
spirit of open data exploration we provide details of our pipeline, release our
supervised machine learning model and code as open source, and make public the
38 candidates we have found in seven sectors. The model can easily be run on
other sectors as is. As part of future work we outline ways to increase the
yield by strengthening some of the steps where we have been conservative and
discarded objects for lack of a datum or two.
