ExoMiner: A Deep Learning Toolkit for Exoplanet Validation

Miguel Martinho; Hamed Valizadegan; Doug Caldwell; Jon Jenkins; Joseph Twicken; Stephen Bryson; Andres Carranza; Fellipe Marcelino; Jennifer Andersson; Kaylie Hausknecht; Laurent Wilkens; Martin Koeling; Nikash Walia; Noa Lubin; Pedro Cesar Lopes Gerum; Patrick Maynard; Sam Donald; Theng Yang; Hongbo Wei; Stuti Agarwal; Joshua Belofsky; Charles Yates; William Zhong; Ashley Raigosa; Saiswaroop Thamminemi; Kunal Malhotra; Eric Liang; Ujjawal Prasad; Adithya Giri; Joshua Ochoa; Aniket Mittal

ExoMiner: A Deep Learning Toolkit for Exoplanet Validation

Miguel Martinho
, Hamed Valizadegan
, Doug Caldwell
, Jon Jenkins
, Joseph Twicken
, Stephen Bryson
, Andres Carranza
, Fellipe Marcelino
, Jennifer Andersson
, Kaylie Hausknecht
, Laurent Wilkens
, Martin Koeling
, Nikash Walia
, Noa Lubin
, Pedro Cesar Lopes Gerum
, Patrick Maynard
, Sam Donald
, Theng Yang
, Hongbo Wei
, Stuti Agarwal

Joshua Belofsky, Charles Yates, William Zhong, Ashley Raigosa, Saiswaroop Thamminemi, Kunal Malhotra, Eric Liang, Ujjawal Prasad, Adithya Giri, Joshua Ochoa, Aniket Mittal

Operations and Supply Chain Management

Research output: Other contribution

Abstract

This open-source Python library is the official implementation of the machine learning and data validation algorithms developed in our NASA-affiliated research article, "Exominer: A highly accurate and explainable deep learning classifier to validate transiting exoplanets" (The Astrophysical Journal, 2022). The library makes our research methods accessible, reproducible, and extensible for the broader astrophysics and data science communities, serving as a key vehicle for the dissemination and impact of our original publication.

Original language	English
Volume	December
State	Published - 2024

Cite this

@misc{14fffbd2c5ae4947954d0c2d52d1ab8c,

title = "ExoMiner: A Deep Learning Toolkit for Exoplanet Validation",

abstract = "This open-source Python library is the official implementation of the machine learning and data validation algorithms developed in our NASA-affiliated research article, {"}Exominer: A highly accurate and explainable deep learning classifier to validate transiting exoplanets{"} (The Astrophysical Journal, 2022). The library makes our research methods accessible, reproducible, and extensible for the broader astrophysics and data science communities, serving as a key vehicle for the dissemination and impact of our original publication.",

author = "Miguel Martinho and Hamed Valizadegan and Doug Caldwell and Jon Jenkins and Joseph Twicken and Stephen Bryson and Andres Carranza and Fellipe Marcelino and Jennifer Andersson and Kaylie Hausknecht and Laurent Wilkens and Martin Koeling and Nikash Walia and Noa Lubin and \{Lopes Gerum\}, \{Pedro Cesar\} and Patrick Maynard and Sam Donald and Theng Yang and Hongbo Wei and Stuti Agarwal and Joshua Belofsky and Charles Yates and William Zhong and Ashley Raigosa and Saiswaroop Thamminemi and Kunal Malhotra and Eric Liang and Ujjawal Prasad and Adithya Giri and Joshua Ochoa and Aniket Mittal",

year = "2024",

language = "English",

volume = "December",

type = "Other",

}

Martinho, M, Valizadegan, H, Caldwell, D, Jenkins, J, Twicken, J, Bryson, S, Carranza, A, Marcelino, F, Andersson, J, Hausknecht, K, Wilkens, L, Koeling, M, Walia, N, Lubin, N, Lopes Gerum, PC, Maynard, P, Donald, S, Yang, T, Wei, H, Agarwal, S, Belofsky, J, Yates, C, Zhong, W, Raigosa, A, Thamminemi, S, Malhotra, K, Liang, E, Prasad, U, Giri, A, Ochoa, J & Mittal, A 2024, ExoMiner: A Deep Learning Toolkit for Exoplanet Validation.

TY - GEN

T1 - ExoMiner: A Deep Learning Toolkit for Exoplanet Validation

AU - Martinho, Miguel

AU - Valizadegan, Hamed

AU - Caldwell, Doug

AU - Jenkins, Jon

AU - Twicken, Joseph

AU - Bryson, Stephen

AU - Carranza, Andres

AU - Marcelino, Fellipe

AU - Andersson, Jennifer

AU - Hausknecht, Kaylie

AU - Wilkens, Laurent

AU - Koeling, Martin

AU - Walia, Nikash

AU - Lubin, Noa

AU - Lopes Gerum, Pedro Cesar

AU - Maynard, Patrick

AU - Donald, Sam

AU - Yang, Theng

AU - Wei, Hongbo

AU - Agarwal, Stuti

AU - Belofsky, Joshua

AU - Yates, Charles

AU - Zhong, William

AU - Raigosa, Ashley

AU - Thamminemi, Saiswaroop

AU - Malhotra, Kunal

AU - Liang, Eric

AU - Prasad, Ujjawal

AU - Giri, Adithya

AU - Ochoa, Joshua

AU - Mittal, Aniket

PY - 2024

Y1 - 2024

N2 - This open-source Python library is the official implementation of the machine learning and data validation algorithms developed in our NASA-affiliated research article, "Exominer: A highly accurate and explainable deep learning classifier to validate transiting exoplanets" (The Astrophysical Journal, 2022). The library makes our research methods accessible, reproducible, and extensible for the broader astrophysics and data science communities, serving as a key vehicle for the dissemination and impact of our original publication.

AB - This open-source Python library is the official implementation of the machine learning and data validation algorithms developed in our NASA-affiliated research article, "Exominer: A highly accurate and explainable deep learning classifier to validate transiting exoplanets" (The Astrophysical Journal, 2022). The library makes our research methods accessible, reproducible, and extensible for the broader astrophysics and data science communities, serving as a key vehicle for the dissemination and impact of our original publication.

UR - https://github.com/nasa/ExoMiner

M3 - Other contribution

VL - December

ER -

ExoMiner: A Deep Learning Toolkit for Exoplanet Validation

Abstract

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Cite this