Many astrophysical phenomena are time-varying, in the sense that their intensity, energy spectrum, and/or the spatial distribution of the emission suddenly change. This paper develops a method for modeling a time series of images. Under the assumption that the arrival times of the photons follow a Poisson process, the data are binned into 4D grids of voxels (time, energy band, and x-y coordinates), and viewed as a time series of non-homogeneous Poisson images. The method assumes that at each time point, the corresponding multiband image stack is an unknown 3D piecewise constant function including Poisson noise. It also assumes that all image stacks between any two adjacent change points (in time domain) share the same unknown piecewise constant function. The proposed method is designed to estimate the number and the locations of all of the change points (in time domain), as well as all of the unknown piecewise constant functions between any pairs of the change points. The method applies the minimum description length principle to perform this task. A practical algorithm is also developed to solve the corresponding complicated optimization problem. Simulation experiments and applications to real data sets show that the proposed method enjoys very promising empirical properties. Applications to two real data sets, the XMM observation of a flaring star and an emerging solar coronal loop, illustrate the usage of the proposed method and the scientific insight gained from it.

许多天体物理现象是随时间变化的，即它们的强度、能谱和/或发射的空间分布突然改变。本文开发了一种对时间序列图像进行建模的方法。在光子到达时间遵循泊松过程的假设下，数据被分箱到 4D 体素网格（时间、能带和x - y坐标），并被视为非齐次泊松图像的时间序列。该方法假设在每个时间点，对应的多波段图像堆栈是一个未知的 3D 分段常数函数，包括泊松噪声。它还假设任何两个相邻变化点（在时域中）之间的所有图像堆栈共享相同的未知分段常数函数。所提出的方法旨在估计所有变化点（在时域中）的数量和位置，以及任何变化点对之间的所有未知分段常数函数。该方法应用最小描述长度原则来执行此任务。还开发了一种实用算法来解决相应的复杂优化问题。模拟实验和实际数据集的应用表明，所提出的方法具有非常有前途的经验特性。对两个真实数据集的应用，即对耀斑恒星和新兴日冕环的 XMM 观测，说明了所提出方法的用法以及从中获得的科学见解。