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

Rao, Sriram and Mahabal, Ashish and Rao, Niyanth and Raghavendra, Cauligi (2021) Nigraha: Machine-learning-based pipeline to identify and evaluate planet candidates from TESS. Monthly Notices of the Royal Astronomical Society, 502 (2). pp. 2845-2858. ISSN 0035-8711. doi:10.1093/mnras/stab203.

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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 (SPOC) pipeline and Quick Look pipeline (QLP) to generate alerts for follow-up. Combined with other efforts from the community, over 2000 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 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.

Item Type:Article
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URLURL TypeDescription Paper ItemData
Rao, Sriram0000-0002-3265-6165
Mahabal, Ashish0000-0003-2242-0244
Additional Information:© 2021 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( Accepted 2021 January 13. Received 2020 December 28; in original form 2020 October 9. Published: 27 January 2021. We thank the anonymous referee whose detailed feedback greatly improved this paper. We thank David Ciardi, Joe Ninan Philip, and Shreyas Vissapragada for insightful comments and feedback on various aspects of this work. We also thank Tim Morton for discussions and feedback on early drafts of this paper. Funding for the TESS mission is provided by NASA’s Science Mission directorate. We acknowledge the use of public TESS Alert data from pipelines at the TESS Science Office and at the TESS Science Processing Operations Center. This research has made use of the Exoplanet Follow-up Observation Program website, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. This paper includes data collected by the TESS mission, which are publicly available from the Mikulski Archive for Space Telescopes (MAST). This research made use of LIGHTKURVE, a Python package for Kepler and TESS data analysis (Lightkurve Collaboration et al. 2018). This work has made use of data from the European Space Agency (ESA) mission Gaia (, processed by the Gaia Data Processing and Analysis Consortium (DPAC, Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. AM acknowledges support from the National Science Foundation (NSF Grant 1640818). Data Availability: The data underlying this article is available for public download on the GitHub repository at Additionally, on the same GitHub repository, we have released the code and models developed as part of this work.
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Gaia Multilateral AgreementUNSPECIFIED
Subject Keywords:methods: data analysis – techniques: photometric – planets and satellites: detection – planetary systems
Issue or Number:2
Record Number:CaltechAUTHORS:20210601-101914695
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Official Citation:Sriram Rao, Ashish Mahabal, Niyanth Rao, Cauligi Raghavendra, Nigraha: Machine-learning-based pipeline to identify and evaluate planet candidates from TESS, Monthly Notices of the Royal Astronomical Society, Volume 502, Issue 2, April 2021, Pages 2845–2858,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109321
Deposited By: Tony Diaz
Deposited On:01 Jun 2021 18:07
Last Modified:01 Jun 2021 18:07

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