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arXiv 2409.04557astro-ph.EPastro-ph.IMcs.LG
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DeepTTV:利用凌星时间变化预测隐藏系外行星的深度学习方法

A Deep Learning Framework for TTV Characterization: A Case Study of the Kepler-88 System

School of Earth and Atmospheric Sciences, Georgia Institute of Technology(佐治亚理工学院地球与大气科学学院);School of Mathematics, Georgia Institute of Technology(佐治亚理工学院数学学院);Center for Relativistic Astrophysics, School of Physics, Georgia Institute of Technology(佐治亚理工学院物理学院相对论天体物理中心);Machine Learning Center, Georgia Institute of Technology(佐治亚理工学院机器学习中心)

Chen Chen, Jiapeng Gao, Gongjie Li, Lingkai Kong, Molei Tao

AI总结:

本文提出DeepTTV,利用基于Transformer的深度学习模型,从TTV与TDV序列预测单凌星系统中隐藏非凌星伴星的参数,质量和偏心率预测误差约2%。

摘要:

凌星时间变化(TTV)提供了有关系外行星质量和轨道性质的丰富信息,这些性质通常通过马尔可夫链蒙特卡洛(MCMC)求解逆问题获得。本文设计了一种新的数据驱动方法,有可能应用于传统MCMC方法难以处理的问题,例如仅有一颗行星发生凌星的情况。具体而言,作者使用深度学习方法,以凌星信息(即TTV和凌星持续时间变化(TDV))作为输入,预测单凌星系统中非凌星伴星的参数。得益于新构建的、基于Transformer的架构,该架构能够从TTV序列数据中提取长程相互作用,这一此前困难的任务现在可以高精度完成,在质量和偏心率上的总体相对误差约为2%。

英文摘要:

Transit timing variation (TTV) provides rich information about the mass and orbital properties of exoplanets, which are often obtained by solving an inverse problem via Markov Chain Monte Carlo (MCMC). In this paper, we design a new data-driven approach, which potentially can be applied to problems that are hard to traditional MCMC methods, such as the case with only one planet transiting. Specifically, we use a deep learning approach to predict the parameters of non-transit companion for the single transit system with transit information (i.e., TTV, and Transit Duration Variation (TDV)) as input. This approach is constructed using the Transformer-based architecture that can extract long-range interactions from TTV sequential data. We focus on two-planet systems based on Kepler-88 as a starting point, with the inner planet fixed to the properties of Kepler-88 and the mass and orbital elements of the outer planet varied. Applying to Kepler-88, we obtained an overall fractional error of $\lesssim$10\% on semi-major axis and eccentricity, and $\sim 10-30\%$ on planet mass (assuming that the stellar and transiting planet parameters are known), based on a training set of a million simulations. When applied to the test set, our model achieves low fractional errors of less than a few percent for systems with TTV semi-amplitudes ranging from $\sim 0.3$ s to $\sim 5$ min. Extending our approach to a broader range of planetary architectures, including systems with substantially weaker or stronger TTV signals and those containing additional planets or stellar companions, remains an important direction for future work.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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