Computing Transiting Exoplanet Parameters with 1D Convolutional Neural Networks
This work addresses the need for more efficient exoplanet analysis in astronomy, though it appears incremental as it applies known neural network techniques to this domain.
The authors tackled the problem of automating the characterization of transiting exoplanets from stellar light curves by developing two 1D convolutional neural network models, which successfully estimated orbital parameters and reduced time and computational costs compared to existing algorithms.
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.