用深度学习从凌星数据推断不可见行星参数，精度达2%。
DeepTTV: Deep Learning Prediction of Hidden Exoplanet From Transit Timing Variations
- 基于Transformer架构分析凌星时间变化序列，捕捉长期关联
- 单颗行星系统中对不可见伴星的质心与偏心率预测误差仅2%
- 适合处理传统方法难解的单次凌星系统问题
凌星时间变化（TTV）为系外行星的质量和轨道特性提供了丰富信息，通常通过马尔可夫链蒙特卡洛（MCMC）求解逆问题获得。本文提出一种新的数据驱动方法，适用于传统MCMC难以处理的情况，如仅有一次凌星的系统。具体而言，利用深度学习模型，以凌星信息（即TTV和凌星持续时间变化TDV）为输入，预测单个凌星系统中不可见伴星的参数。得益于新构建的基于Transformer的架构，该方法能够有效提取TTV序列中的长程相互作用，显著提升预测精度，在质量和偏心率预测上整体相对误差约为2%。
原文摘要 · Abstract (English)
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. Thanks to a newly constructed \textit{Transformer}-based architecture that can extract long-range interactions from TTV sequential data, this previously difficult task can now be accomplished with high accuracy, with an overall fractional error of $\sim$2\% on mass and eccentricity.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。