X-ray spectral fitting of astronomical sources requires convolving the intrinsic spectrum or model with the instrumental response. Standard forward modeling techniques have proven success in recovering the underlying physical parameters in moderate to high signal-to-noise regimes; however, they struggle to achieve the same level of accuracy in low signal-to-noise regimes. Additionally, the use of machine learning techniques on X-ray spectra requires access to the intrinsic spectrum. Therefore, the measured spectrum must be effectively deconvolved from the instrumental response. In this note, we explore numerical methods for inverting the matrix equation describing X-ray spectral convolution. We demonstrate that traditional methods are insufficient to recover the intrinsic X-ray spectrum and argue that a novel approach is required.

天文源的 X 射线光谱拟合需要将固有光谱或模型与仪器响应进行卷积。标准的正向建模技术已证明成功地恢复了中高信噪比的基础物理参数；然而，它们很难在低信噪比的情况下达到相同的精度水平。此外，在 X 射线光谱上使用机器学习技术需要访问本征光谱。因此，测量的光谱必须从仪器响应中有效地去卷积。在本说明中，我们探索了用于对描述 X 射线光谱卷积的矩阵方程求逆的数值方法。我们证明传统方法不足以恢复固有的 X 射线光谱，并认为需要一种新方法。