Transits of habitable planets around solar-like stars are expected to be
shallow, and to have long periods, which means low information content. The
current bottleneck in the detection of such transits is caused in large part by
the presence of red (correlated) noise in the light curves obtained from the
dedicated space telescopes. Based on the groundbreaking results deep learning
achieves in many signal and image processing applications, we propose to use
deep neural networks to solve this problem. We present a feasibility study, in
which we applied a convolutional neural network on a simulated training set.
The training set comprised light curves received from a hypothetical
high-cadence space-based telescope. We simulated the red noise by using
Gaussian Processes with a wide variety of hyperparameters. We then tested the
network on a completely different test set simulated in the same way. Our study
proves that very difficult cases can indeed be detected. Furthermore, we show
how detection trends can be studied, and detection biases be quantified. We
have also checked the robustness of the neural-network performance against
practical artifacts such as outliers and discontinuities, which are known to
affect space-based high-cadence light curves. Future work will allow us to use
the neural networks to characterize the transit model and identify individual
transits. This new approach will certainly be an indispensable tool for the
detection of habitable planets in the future planet-detection space missions
such as PLATO.

使用深度神经网络解决红色（相关）噪声问题，以便更好地侦测未来行星探测空间任务（如 PLATO）中的可居住星球。