{'fraction_non_alphanumeric': 0.015625, 'fraction_numerical': 0.0, 'mean_word_length': 5.722222222222222, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Time-dependent Reconstruction of Nonstationary Objects with Tomographic or Interferometric Measurements

In a number of astrophysical applications one tries to determine the two-dimensional or three-dimensional structure of an object from a time series of measurements. While most methods used for reconstruction assume that the object is static, the data are often acquired over a time interval during which the object may change significantly. This problem can be addressed with time-dependent reconstruction methods such as Kalman filtering, which models the temporal evolution of the unknown object as a random walk that may or may not have a deterministic component. Time-dependent reconstructions of a hydrodynamic simulation from its line-integral projections are presented. In these examples standard reconstructions based on the static assumption are poor, while good quality reconstructions are obtained from a regularized Kalman estimate. Implications for various astrophysical applications, including tomography of the solar corona and radio aperture synthesis, are discussed.