论文标题
部分可观测时空混沌系统的无模型预测
X-ray Reverberation Mapping of Ark 564 using Gaussian Process Regression
论文作者
论文摘要
ARK 564是一种极端的高迪丁顿窄线Seyfert 1 Galaxy，以最亮，最快的柔软X射线AGN闻名，并且具有最低温度的冠状动脉之一。在这里，我们提出了410 ks nustar的观察和两个115 ks XMM-Newton对此独特来源的观察结果，该观察结果揭示了非常强，相对宽阔的铁线。我们首先使用高斯流程来插入Nustar差距，从而计算傅立叶分辨时间滞后，从而在AGN时间安排中实施了多任务学习的首次使用。通过同时拟合时间滞后和磁通光谱与相对论的回响模型的复发，我们将质量限制为$ 2.3^{+2.6} _ { - 1.3} \ times 10^6m_ \ odot $，尽管需要其他组件来描述此源中突出的软件。这些结果激发了机器学习，傅立叶分辨时间和混响模型的发展的未来组合。
Ark 564 is an extreme high-Eddington Narrow-line Seyfert 1 galaxy, known for being one of the brightest, most rapidly variable soft X-ray AGN, and for having one of the lowest temperature coronae. Here we present a 410-ks NuSTAR observation and two 115-ks XMM-Newton observations of this unique source, which reveal a very strong, relativistically broadened iron line. We compute the Fourier-resolved time lags by first using Gaussian processes to interpolate the NuSTAR gaps, implementing the first employment of multi-task learning for application in AGN timing. By fitting simultaneously the time lags and the flux spectra with the relativistic reverberation model RELTRANS, we constrain the mass at $2.3^{+2.6}_{-1.3} \times 10^6M_\odot$, although additional components are required to describe the prominent soft excess in this source. These results motivate future combinations of machine learning, Fourier-resolved timing, and the development of reverberation models.
