Abstract
Machine Learning is believed to be the solution for reducing the amount of time and computational cost required to analyze the large volume of light curves obtained from different surveys in order to detect transit-like signals using the transit method. This technique consists in detecting periodic dimmings in stellar light curves due to the presence of an orbiting exoplanet. We created a 1D Convolutional Neural Network model which was trained, validated and tested with simulated light curves that mimic those expected for the Kepler Space Telescope in its extended mission (K2). Our light curve simulator considers different stellar variability phenomena, such as rotations, pulsations and flares, which along with the stellar noise expected for K2 data, hinders the transit signal detection, as in real data.
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Notes
- 1.
Stellar magnitude is a measure of the stellar brightness as observed from the Earth.
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Iglesias Álvarez, S. et al. (2023). Transiting Exoplanet Detection Through 1D Convolutional Neural Networks. In: Jove, E., Zayas-Gato, F., Michelena, Á., Calvo-Rolle, J.L. (eds) Distributed Computing and Artificial Intelligence, Special Sessions II - Intelligent Systems Applications, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-031-38616-9_6
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