arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2402.13673astro-ph.EPastro-ph.IMcs.LG
收藏

利用一维卷积神经网络计算凌星系外行星参数

Computing Transiting Exoplanet Parameters with 1D Convolutional Neural Networks

Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA)(阿斯图里亚斯空间科学与技术大学研究所);Universidad de Oviedo(奥维耶多大学)

Santiago Iglesias Álvarez, Enrique Díez Alonso, María Luisa Sánchez Rodríguez, Javier Rodríguez Rodríguez, Saúl Pérez Fernández, Francisco Javier de Cos Juez

AI总结:

本文提出两个一维卷积神经网络模型,分别从完整和相位折叠的模拟光变曲线中估计凌星系外行星的轨道周期、半长轴及行星与恒星半径比平方,并在TESS真实数据上验证其有效性,显著降低了探测与表征的时间和计算成本。

摘要:

凌星法通过分析恒星光变曲线,能够探测和表征行星系统。卷积神经网络似乎为自动化这些分析提供了一种可行的解决方案。在本研究中,提出了两个一维卷积神经网络模型,它们使用注入了类凌星信号的模拟光变曲线进行训练。一个模型在完整光变曲线上运行,用于估计轨道周期;另一个模型在相位折叠光变曲线上运行,用于估计轨道半长轴以及行星与恒星半径比的平方。两个模型均在来自TESS光变曲线且已确认存在行星的真实数据上进行了测试,以确保它们能够处理真实数据。所获得的结果表明,一维卷积神经网络能够根据其宿主恒星去除趋势后的光变曲线来表征凌星系外行星,并且与现有的探测和表征算法相比,进一步减少了所需的时间和计算成本。

英文摘要:

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.

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。

↑