Open AccessExploiting Kepler’s Heritage: A Transfer Learning Approach for Identifying Exoplanets’ Transits in TESS Data
Author(s) -
Stefano Fiscale,
Angelo Ciaramella,
L. Inno,
G. Covone,
Alessio Ferone,
A. Rotundi,
Kelsey Hoffman,
Elisa V. Quintana,
Jason F. Rowe,
Ida Bifulco,
Luca Cacciapuoti,
F. Gallo,
Riccardo M. Ienco
Publication year - 2021
Publication title -
research notes of the aas
Language(s) - English
Resource type - Journals
ISSN - 2515-5172
DOI - 10.3847/2515-5172/abf56b
Subject(s) - exoplanet , kepler , planet , light curve , vetting , computer science , artificial neural network , data set , artificial intelligence , set (abstract data type) , astronomy , physics , computer security , programming language
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.