论文标题
具有神经网络的AGN X射线光谱
AGN X-ray Spectroscopy with Neural Networks
论文作者
论文摘要
我们探讨了使用机器学习直接从AGN X射线光谱估算物理参数的可能性，而无需计算昂贵的光谱拟合。具体而言，我们考虑调查质量数据，而不是长期尖锐的观察结果，以确保这种方法在最有可能应用的制度中起作用。我们使用温暖的吸收剂模拟AGN的雅典娜WFI光谱，并训练简单的神经网络，以估计吸收剂的电离和色谱柱密度。我们发现，这种方法可以使光谱拟合的准确性可相当，而不会因贴合的最低效果而导致异常值的风险，并且速度的提高了三个数量级。我们还证明，在将其输入神经网之前，使用主组件分析降低数据的维度可以显着提高参数估计的准确性可忽略不计的计算成本，同时还允许使用更简单的网络体系结构。
We explore the possibility of using machine learning to estimate physical parameters directly from AGN X-ray spectra without needing computationally expensive spectral fitting. Specifically, we consider survey quality data, rather than long pointed observations, to ensure that this approach works in the regime where it is most likely to be applied. We simulate Athena WFI spectra of AGN with warm absorbers, and train simple neural networks to estimate the ionisation and column density of the absorbers. We find that this approach can give comparable accuracy to spectral fitting, without the risk of outliers caused by the fit sticking in a false minimum, and with an improvement of around three orders of magnitude in speed. We also demonstrate that using principal component analysis to reduce the dimensionality of the data prior to inputting it into the neural net can significantly increase the accuracy of the parameter estimation for negligible computational cost, while also allowing a simpler network architecture to be used.
