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。

使用一维卷积神经网络计算行星凌星参数

Computing Transiting Exoplanet Parameters with 1D Convolutional Neural  Networks

The transit method is one of the most relevant exoplanet detection
techniques, which consists of detecting periodic eclipses in the light curves
of stars. This is not always easy due to the presence of noise in the light
curves, which is induced, for example, by the response of a telescope to
stellar flux. For this reason, we aimed to develop an artificial neural network
model that is able to detect these transits in light curves obtained from
different telescopes and surveys. We created artificial light curves with and
without transits to try to mimic those expected for the extended mission of the
Kepler telescope (K2) in order to train and validate a 1D convolutional neural
network model, which was later tested, obtaining an accuracy of 99.02 % and an
estimated error (loss function) of 0.03. These results, among others, helped to
confirm that the 1D CNN is a good choice for working with non-phased-folded
Mandel and Agol light curves with transits. It also reduces the number of light
curves that have to be visually inspected to decide if they present
transit-like signals and decreases the time needed for analyzing each (with
respect to traditional analysis).

开发了一个能够检测来自不同望远镜和调查中的光曲线中的短时（transits）的人工神经网络模型，结果表明 1D CNN 是处理非相位折叠的光曲线具有短时的良好选择，并减少了传统分析所需要的时间。

一维卷积神经网络检测行星凌变

One-dimensional Convolutional Neural Networks for Detecting Transiting  Exoplanets

This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase
Folding and Convolutional Neural Network (CNN) system to detect exoplanets
using the transit method. We devise a fast folding algorithm parallelized on a
GPU to amplify low signal-to-noise ratio transit signals, allowing a search at
high precision and speed. A CNN trained on two million synthetic light curves
reports a score indicating the likelihood of a planetary signal at each period.
GPFC improves on speed by three orders of magnitude over the predominant
Box-fitting Least Squares (BLS) method. Our simulation results show GPFC
achieves 97% training accuracy, higher true positive rate at the same false
positive rate of detection, and higher precision at the same recall rate when
compared to BLS. GPFC recovers 100% of known ultra-short-period planets in
Kepler light curves from a blind search. These results highlight the promise of
GPFC as an alternative approach to the traditional BLS algorithm for finding
new transiting exoplanets in data taken with Kepler and other space transit
missions such as K2, TESS and future PLATO and Earth 2.0.

GPFC 是一种新颖的图形处理单元（GPU）相位折叠和卷积神经网络（CNN）系统，用于利用凌日法检测系外行星。该系统采用在 GPU 上并行化的快速折叠算法来放大信噪比低的凌日信号，从而以高精度和速度进行搜索。经过对两百万个合成光曲线进行训练的 CNN 报告了每个周期可能性的得分，GPFC 在速度上比现有的盒形拟合最小二乘（BLS）方法快了三个数量级。模拟结果显示，与 BLS 相比，GPFC 实现了 97% 的训练准确性，在相同的误报率下具有更高的真阳性率和在相同的检测率下具有更高的准确性。在 Kepler 光曲线中进行盲搜索时，GPFC 可以完全恢复已知的超短周期行星。这些结果突显了 GPFC 作为替代传统 BLS 算法在 Kepler 和其他空间凌日任务（如 K2、TESS 和未来的 PLATO 和 Earth 2.0）的数据中寻找新的凌日行星的潜力。