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

TL;DR

The paper introduces Nigraha, a machine-learning pipeline that enhances the detection of planet candidates from TESS data, especially Earth-like planets, by identifying missed signals through advanced vetting and open-source tools.

Contribution

Nigraha provides a novel, open-source machine learning pipeline that complements existing TESS analyses to identify additional high-confidence planet candidates, including shallow transits.

Findings

Identified 38 new planet candidates in seven sectors.

Successfully detected high SNR shallow transits missed by previous methods.

Pipeline is adaptable for use on other sectors and datasets.

Abstract

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.