In the last decade, exoplanets space missions started to collect a huge amount of photometric observations, with over ∼1,000,000 new light curves generated every month from the Transiting Exoplanet Survey Satellite (TESS) full-frame images alone. In order to analyze such an unprecedented volume of data, automated planet-candidate detection has become an appreciable replacement to human vetting. In this work, we present a Machine Learning approach, based on Deep Neural Networks, to perform a binary classification of TESS light curves in terms of planet candidate and not-planet. Since few TESS labeled data exist to date, we pre-train the network with Kepler DR24 data set, including ≳15,000 labeled light curves. Our pre-trained model is then tested on ExoFOP data, showing an appreciable gain in terms of reliability with respect to a randomly initialized model.

Exploiting Kepler’s Heritage: A Transfer Learning Approach for Identifying Exoplanets’ Transits in TESS Data

Fiscale, Stefano
Membro del Collaboration Group
;
Ciaramella, Angelo
Membro del Collaboration Group
;
Inno, Laura
Membro del Collaboration Group
;
Ferone, Alessio
Membro del Collaboration Group
;
Rotundi, Alessandra
Membro del Collaboration Group
;
Gallo, Francesco
Membro del Collaboration Group
;
2021

Abstract

In the last decade, exoplanets space missions started to collect a huge amount of photometric observations, with over ∼1,000,000 new light curves generated every month from the Transiting Exoplanet Survey Satellite (TESS) full-frame images alone. In order to analyze such an unprecedented volume of data, automated planet-candidate detection has become an appreciable replacement to human vetting. In this work, we present a Machine Learning approach, based on Deep Neural Networks, to perform a binary classification of TESS light curves in terms of planet candidate and not-planet. Since few TESS labeled data exist to date, we pre-train the network with Kepler DR24 data set, including ≳15,000 labeled light curves. Our pre-trained model is then tested on ExoFOP data, showing an appreciable gain in terms of reliability with respect to a randomly initialized model.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/103193
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