发表机构
Max Planck Institute for Astrophysics; Faculty of Physics, Ludwig-Maximilians-Universität München; University Observatory, Faculty of Physics, Ludwig-Maximilians-Universität（马克斯·普朗克天体物理学研究所; 慕尼黑大学物理学院; 慕尼黑大学物理学院天文台）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于信息场论的贝叶斯推断结合空间变化PSF表示，用于X射线图像去卷积，在合成基准上优于Richardson-Lucy算法，并在仙后座A真实观测中实现更清晰重建。
AI 中文摘要
X射线天文台会在光子计数数据中引入必须精确考虑的仪器效应。特别是，空间变化的点扩散函数（PSF）和散粒噪声构成了一个非平凡的逆问题。我们应用基于信息场论（IFT）的贝叶斯推断，结合快速的空间变化PSF表示，在最小化物理动机先验假设下推断X射线通量的后验均值和不确定性，从而消除或减少这些仪器效应。首先，忽略空间PSF变化性，基于IFT的去卷积在合成示例上使用SSIM、RMSE和NLL作为评估指标，与Richardson-Lucy（RL）算法的三种变体进行基准比较。其次，评估了一种插值补丁卷积方案在模拟PSF数据上准确表示复杂的空间变化Chandra PSF的能力。最后，将去模糊和空间变化PSF表示相结合，应用于超新星遗迹仙后座A的真实Chandra观测数据。在合成基准测试中，基于IFT的去卷积优于所有测试的RL变体。补丁卷积方案准确恢复了Chandra PSF的复杂结构。应用于仙后座A时，重建图像在视觉上比曝光校正数据更清晰，数据残差与纯噪声一致，表明仪器和天空模型均准确。与高分辨率参考数据集的比较证实了这一印象。所提出的框架能够从噪声数据中去除空间变化的PSF，并为未来基于贝叶斯框架固有不确定性量化原理，开展多次观测或不同X射线仪器之间交叉校准的研究开辟了途径。
英文摘要
X-ray observatories introduce unwanted instrumental effects into photon count data that must be accurately accounted for. In particular, the spatially variant point spread function (PSF) and shot noise pose a non-trivial inverse problem. We apply Bayesian inference grounded in information field theory (IFT) combined with a fast spatially variant PSF representation to infer the posterior mean and uncertainty of the X-ray flux under a minimal set of physically motivated prior assumptions, thereby removing or reducing these instrumental effects. First, ignoring spatial PSF variability, the IFT-based deconvolution is benchmarked on a synthetic example against three variants of the Richardson-Lucy (RL) algorithm using SSIM, RMSE, and NLL as evaluation metrics. Second, the ability of an interpolated patch-based convolution scheme to accurately represent the complex, spatially variant Chandra PSF is assessed on simulated PSF data. Finally, deblurring and spatially variant PSF representation are combined and applied to real Chandra observations of the supernova remnant Cassiopeia A. The IFT-based deconvolution outperforms all tested RL variants on the synthetic benchmark. The patch-based convolution scheme accurately recovers the complex structure of the Chandra PSF. Applied to Cassiopeia A, the reconstructed image appears visually sharper than the exposure-corrected data, and the data residuals are consistent with pure noise, indicating an accurate model of both the instrument and the sky. A comparison with a highly resolved reference dataset confirms this impression. The presented framework enables the removal of spatially variant PSFs from noisy data and opens avenues for future work on cross-calibration between multiple observations or different X-ray instruments, building on the principled uncertainty quantification inherent to the Bayesian framework.
Commentssubmitted to Astronomy and Astrophysics
