This paper presents the detection of a periodic dimming event in the
lightcurve of the G1.5IV-V type star KIC 1718360. This is based on
visible-light observations conducted by both the TESS and Kepler space
telescopes. Analysis of the data points toward a possible orbiting body with a
radius of approximately 1.048 Earth Radii with a period of 2.938 days, as well
as a semi-major axis of 0.04 AU. The initial observation was made in Kepler
Quarter 16 data using the One-Class SVM machine learning method. Subsequent
observations by the TESS space telescope corroborate these findings. While
still requiring further data to validate, these results may contribute to a
growing body of data of Earthlike planets with short-period orbits.

这篇论文通过 TESS 和 Kepler 空间望远镜的可见光观测，提出了对 KIC 1718360 这颗 G1.5IV-V 型恒星光变曲线中的定期暗淡事件的检测。数据分析表明可能存在一个半径约为 1.048 地球半径、轨道周期为 2.938 天的天体，以及一个 0.04 天文单位的半长轴。初步观测使用了 One-Class SVM 机器学习方法，在 Kepler 的第 16 季数据上进行。TESS 空间望远镜的后续观测证实了这些发现。虽然仍需要进一步数据验证，但这些结果可能有助于积累更多短周期轨道的类地行星数据。

利用机器学习探测 KIC 1718360 可能存在的太阳系外行星

The Detection of a Possible Exoplanet Orbiting KIC 1718360 Using Machine  Learning

Current space-based missions, such as the Transiting Exoplanet Survey
Satellite (TESS), provide a large database of light curves that must be
analysed efficiently and systematically. In recent years, deep learning (DL)
methods, particularly convolutional neural networks (CNN), have been used to
classify transit signals of candidate exoplanets automatically. However, CNNs
have some drawbacks; for example, they require many layers to capture
dependencies on sequential data, such as light curves, making the network so
large that it eventually becomes impractical. The self-attention mechanism is a
DL technique that attempts to mimic the action of selectively focusing on some
relevant things while ignoring others. Models, such as the Transformer
architecture, were recently proposed for sequential data with successful
results. Based on these successful models, we present a new architecture for
the automatic classification of transit signals. Our proposed architecture is
designed to capture the most significant features of a transit signal and
stellar parameters through the self-attention mechanism. In addition to model
prediction, we take advantage of attention map inspection, obtaining a more
interpretable DL approach. Thus, we can identify the relevance of each element
to differentiate a transit signal from false positives, simplifying the manual
examination of candidates. We show that our architecture achieves competitive
results concerning the CNNs applied for recognizing exoplanetary transit
signals in data from the TESS telescope. Based on these results, we demonstrate
that applying this state-of-the-art DL model to light curves can be a powerful
technique for transit signal detection while offering a level of
interpretability.

本研究提出了一种新的基于深度学习（DL）的自动分类轨道信号的架构，并通过自我关注机制来捕捉信号与恒星参数的最重要特征，并利用注意力映射检验各元素的相关性以简化候选者的手动检查。结果表明，此 DL 模型应用于轻曲线数据可以作为探测轨道信号的一种强大技术，并提供了一定的可解释性。