Computing Transiting Exoplanet Parameters with 1D Convolutional Neural
  Networks
Santiago Iglesias Álvarez Enrique Díez Alonso María Luisa Sánchez Rodríguez Javier Rodríguez Rodríguez Saúl Pérez Fernández Francisco Javier de Cos Juez
The transit method allows the detection and characterization of planetary
systems by analyzing stellar light curves. Convolutional neural networks appear
to offer a viable solution for automating these analyses. In this research, two
1D convolutional neural network models, which work with simulated light curves
in which transit-like signals were injected, are presented. One model operates
on complete light curves and estimates the orbital period, and the other one
operates on phase-folded light curves and estimates the semimajor axis of the
orbit and the square of the planet-to-star radius ratio. Both models were
tested on real data from TESS light curves with confirmed planets to ensure
that they are able to work with real data. The results obtained show that 1D
CNNs are able to characterize transiting exoplanets from their host star's
detrended light curve and, furthermore, reducing both the required time and
computational costs compared with the current detection and characterization
algorithms.