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.

通过使用模拟的光度曲线注入了类似于行星运动信号的两个一维卷积神经网络模型，在真实数据测试中证明了这些模型能够从恒星去除趋势的光度曲线中表征恒星行星的性质，并且相较于当前的探测和描述算法，降低了所需时间和计算成本。