Transiting Exoplanet Discovery Using Machine Learning Techniques: A Survey. Jara-Maldonado, M., Alarcon-Aquino, V., Rosas-Romero, R., Starostenko, O., & Ramirez-Cortes, J., M. Earth Science Informatics, 13(3):573-600, Springer, 2020. ![Transiting Exoplanet Discovery Using Machine Learning Techniques: A Survey [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 5 downloads Spatial missions such as the Kepler mission, and the Transiting Exoplanet Survey Satellite (TESS) mission, have encouraged data scientists to analyze light curve datasets. The purpose of analyzing these data is to look for planet transits, with the aim of discovering and validating exoplanets, which are planets found outside our Solar System. Furthermore, transiting exoplanets can be better characterized when light curves and radial velocity curves are available. The manual examination of these datasets is a task that requires big quantities of time and effort, and therefore is prone to errors. As a result, the application of machine learning methods has become more common on exoplanet discovery and categorization research. This survey presents an analysis on different exoplanet transit discovery algorithms based on machine learning, some of which even found new exoplanets. The analysis of these algorithms is divided into four steps, namely light curve preprocessing, possible exoplanet signal detection, and identification of the detected signal to decide whether it belongs to an exoplanet or not. We propose a model to create synthetic datasets of light curves, and we compare the performance of several machine learning models used to identify transit exoplanets, with inputs preprocessed with and without using the Discrete Wavelet Transform (DWT). Our experimental results allow us to conclude that multiresolution analysis in the time-frequency domain can improve exoplanet signal identification, because of the characteristics of light curves and transiting exoplanet signals.
@article{
title = {Transiting Exoplanet Discovery Using Machine Learning Techniques: A Survey},
type = {article},
year = {2020},
keywords = {Artificial intelligence,Deep learning,Discrete wavelet transform,Exoplanets,Light curves,Machine learning,Multiresolution analysis,Transits},
pages = {573-600},
volume = {13},
websites = {https://link.springer.com/10.1007/s12145-020-00464-7},
publisher = {Springer},
id = {46622163-c757-358e-8a12-fbc7e61797c7},
created = {2022-08-29T17:42:19.890Z},
accessed = {2021-10-23},
file_attached = {false},
profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
last_modified = {2022-08-29T17:42:19.890Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {true},
hidden = {false},
source_type = {Article},
private_publication = {false},
abstract = {Spatial missions such as the Kepler mission, and the Transiting Exoplanet Survey Satellite (TESS) mission, have encouraged data scientists to analyze light curve datasets. The purpose of analyzing these data is to look for planet transits, with the aim of discovering and validating exoplanets, which are planets found outside our Solar System. Furthermore, transiting exoplanets can be better characterized when light curves and radial velocity curves are available. The manual examination of these datasets is a task that requires big quantities of time and effort, and therefore is prone to errors. As a result, the application of machine learning methods has become more common on exoplanet discovery and categorization research. This survey presents an analysis on different exoplanet transit discovery algorithms based on machine learning, some of which even found new exoplanets. The analysis of these algorithms is divided into four steps, namely light curve preprocessing, possible exoplanet signal detection, and identification of the detected signal to decide whether it belongs to an exoplanet or not. We propose a model to create synthetic datasets of light curves, and we compare the performance of several machine learning models used to identify transit exoplanets, with inputs preprocessed with and without using the Discrete Wavelet Transform (DWT). Our experimental results allow us to conclude that multiresolution analysis in the time-frequency domain can improve exoplanet signal identification, because of the characteristics of light curves and transiting exoplanet signals.},
bibtype = {article},
author = {Jara-Maldonado, Miguel and Alarcon-Aquino, Vicente and Rosas-Romero, Roberto and Starostenko, Oleg and Ramirez-Cortes, Juan Manuel},
doi = {10.1007/s12145-020-00464-7},
journal = {Earth Science Informatics},
number = {3}
}
Downloads: 5
{"_id":"qCXmGHSKaQ9XxepXZ","bibbaseid":"jaramaldonado-alarconaquino-rosasromero-starostenko-ramirezcortes-transitingexoplanetdiscoveryusingmachinelearningtechniquesasurvey-2020","authorIDs":[],"author_short":["Jara-Maldonado, M.","Alarcon-Aquino, V.","Rosas-Romero, R.","Starostenko, O.","Ramirez-Cortes, J., M."],"bibdata":{"title":"Transiting Exoplanet Discovery Using Machine Learning Techniques: A Survey","type":"article","year":"2020","keywords":"Artificial intelligence,Deep learning,Discrete wavelet transform,Exoplanets,Light curves,Machine learning,Multiresolution analysis,Transits","pages":"573-600","volume":"13","websites":"https://link.springer.com/10.1007/s12145-020-00464-7","publisher":"Springer","id":"46622163-c757-358e-8a12-fbc7e61797c7","created":"2022-08-29T17:42:19.890Z","accessed":"2021-10-23","file_attached":false,"profile_id":"940dd160-7d67-3a5f-b9f8-935da0571367","group_id":"92fccab2-8d44-33bc-b301-7b94bb18523c","last_modified":"2022-08-29T17:42:19.890Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"source_type":"Article","private_publication":false,"abstract":"Spatial missions such as the Kepler mission, and the Transiting Exoplanet Survey Satellite (TESS) mission, have encouraged data scientists to analyze light curve datasets. The purpose of analyzing these data is to look for planet transits, with the aim of discovering and validating exoplanets, which are planets found outside our Solar System. Furthermore, transiting exoplanets can be better characterized when light curves and radial velocity curves are available. The manual examination of these datasets is a task that requires big quantities of time and effort, and therefore is prone to errors. As a result, the application of machine learning methods has become more common on exoplanet discovery and categorization research. This survey presents an analysis on different exoplanet transit discovery algorithms based on machine learning, some of which even found new exoplanets. The analysis of these algorithms is divided into four steps, namely light curve preprocessing, possible exoplanet signal detection, and identification of the detected signal to decide whether it belongs to an exoplanet or not. We propose a model to create synthetic datasets of light curves, and we compare the performance of several machine learning models used to identify transit exoplanets, with inputs preprocessed with and without using the Discrete Wavelet Transform (DWT). Our experimental results allow us to conclude that multiresolution analysis in the time-frequency domain can improve exoplanet signal identification, because of the characteristics of light curves and transiting exoplanet signals.","bibtype":"article","author":"Jara-Maldonado, Miguel and Alarcon-Aquino, Vicente and Rosas-Romero, Roberto and Starostenko, Oleg and Ramirez-Cortes, Juan Manuel","doi":"10.1007/s12145-020-00464-7","journal":"Earth Science Informatics","number":"3","bibtex":"@article{\n title = {Transiting Exoplanet Discovery Using Machine Learning Techniques: A Survey},\n type = {article},\n year = {2020},\n keywords = {Artificial intelligence,Deep learning,Discrete wavelet transform,Exoplanets,Light curves,Machine learning,Multiresolution analysis,Transits},\n pages = {573-600},\n volume = {13},\n websites = {https://link.springer.com/10.1007/s12145-020-00464-7},\n publisher = {Springer},\n id = {46622163-c757-358e-8a12-fbc7e61797c7},\n created = {2022-08-29T17:42:19.890Z},\n accessed = {2021-10-23},\n file_attached = {false},\n profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},\n group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},\n last_modified = {2022-08-29T17:42:19.890Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n source_type = {Article},\n private_publication = {false},\n abstract = {Spatial missions such as the Kepler mission, and the Transiting Exoplanet Survey Satellite (TESS) mission, have encouraged data scientists to analyze light curve datasets. The purpose of analyzing these data is to look for planet transits, with the aim of discovering and validating exoplanets, which are planets found outside our Solar System. Furthermore, transiting exoplanets can be better characterized when light curves and radial velocity curves are available. The manual examination of these datasets is a task that requires big quantities of time and effort, and therefore is prone to errors. As a result, the application of machine learning methods has become more common on exoplanet discovery and categorization research. This survey presents an analysis on different exoplanet transit discovery algorithms based on machine learning, some of which even found new exoplanets. The analysis of these algorithms is divided into four steps, namely light curve preprocessing, possible exoplanet signal detection, and identification of the detected signal to decide whether it belongs to an exoplanet or not. We propose a model to create synthetic datasets of light curves, and we compare the performance of several machine learning models used to identify transit exoplanets, with inputs preprocessed with and without using the Discrete Wavelet Transform (DWT). Our experimental results allow us to conclude that multiresolution analysis in the time-frequency domain can improve exoplanet signal identification, because of the characteristics of light curves and transiting exoplanet signals.},\n bibtype = {article},\n author = {Jara-Maldonado, Miguel and Alarcon-Aquino, Vicente and Rosas-Romero, Roberto and Starostenko, Oleg and Ramirez-Cortes, Juan Manuel},\n doi = {10.1007/s12145-020-00464-7},\n journal = {Earth Science Informatics},\n number = {3}\n}","author_short":["Jara-Maldonado, M.","Alarcon-Aquino, V.","Rosas-Romero, R.","Starostenko, O.","Ramirez-Cortes, J., M."],"urls":{"Website":"https://link.springer.com/10.1007/s12145-020-00464-7"},"biburl":"https://bibbase.org/service/mendeley/940dd160-7d67-3a5f-b9f8-935da0571367","bibbaseid":"jaramaldonado-alarconaquino-rosasromero-starostenko-ramirezcortes-transitingexoplanetdiscoveryusingmachinelearningtechniquesasurvey-2020","role":"author","keyword":["Artificial intelligence","Deep learning","Discrete wavelet transform","Exoplanets","Light curves","Machine learning","Multiresolution analysis","Transits"],"metadata":{"authorlinks":{}},"downloads":5},"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/940dd160-7d67-3a5f-b9f8-935da0571367","creationDate":"2020-06-11T01:54:45.687Z","downloads":5,"keywords":["artificial intelligence","deep learning","discrete wavelet transform","exoplanets","light curves","machine learning","multiresolution analysis","transits"],"search_terms":["transiting","exoplanet","discovery","using","machine","learning","techniques","survey","jara-maldonado","alarcon-aquino","rosas-romero","starostenko","ramirez-cortes"],"title":"Transiting Exoplanet Discovery Using Machine Learning Techniques: A Survey","year":2020,"dataSources":["uTykyhFv6T7J2dX6z","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"]}