用于干涉测量数据全斯托克斯动态成像重建的群体智能
Swarm intelligence for full Stokes dynamic imaging reconstruction of interferometric data
AI总结：
针对VLBI成像不适定逆问题，提出无需空间网格化的多目标粒子群优化算法，可同时实现静态和动态全斯托克斯偏振成像，无需参数搜索且不受像素数限制。
摘要：
在甚长基线干涉测量（VLBI）中，多台天线的组合可合成一台直径更大的虚拟望远镜，因此分辨率高于单台天线。但由于阵列的稀疏特性，从观测数据中恢复图像是一个极具挑战性的不适定逆问题。VLBI领域不仅关注从干涉数据中恢复总强度图像，还希望获得偏振域和时域的结果，目前仅有少数算法能同时在所有这些域中工作。尤其是那些考虑了静态总强度成像、时间可变性和偏振测量相关的多种惩罚项的基于优化的算法，都受限于目标函数域的网格划分。在本研究中，我们提出了一种新算法——多目标粒子群优化，它无需任何空间网格化即可恢复最优权重，并能得到每个作用项的边际贡献。为此，我们将多目标优化与粒子群元启发式算法相结合，让权重群体共同收敛至最优位置。我们使用聚焦于事件视界望远镜合作组织（Event Horizon Telescope Collaboration）及其规划中的后继项目仪器配置的代表性合成数据集评估了该算法。即使数据相对稀疏且存在一系列真实的数据损毁情况，我们仍成功恢复了真实值影片的偏振、静态和时间动态特征。这是一种新颖、快速、无需权重空间网格化的算法，可成功实现静态和动态偏振重建。与正则化最大似然方法相比，它无需进行参数搜索，且不像近期提出的多目标成像算法那样受像素数量限制。
英文摘要：
In very long baseline interferometry (VLBI) the combination of multiple antennas permits the synthesis of a virtual telescope with a larger diameter and consequently higher resolution than the individual antennae. Yet, due to the sparse nature of the array, recovering an image from the observed data is a challenging ill-posed inverse problem. The VLBI community is interested in not only recovering an image in total intensity from interferometric data, but also to obtain results in the polarimetric and the temporal domain. Only a few algorithms are able to work in all these domains simultaneously. In particular, the algorithms based on optimization that consider various penalty terms specific to static total intensity imaging, time-variability and polarimetry are restricted to grids the domain of the objective function. In this work we present a novel algorithm, multiobjective particle swarm optimization, that is able to recover the optimal weights without any space-gridding, and to obtain the marginal contribution of each the playing terms. To this end, we utilize multiobjective optimization together with particle swarm metaheuristics. We let the swarm of weights to converge together to the best position. We evaluate our algorithm with representative synthetic data sets focused on the instrumental configuration of the Event Horizon Telescope Collaboration and its planned successors. We successfully recover the polarimetric, static and time-dynamic signature of the ground truth movie, even with relative sparsity, and a set of realistic data corruptions. This is a novel, fast, weighting space gridding-free algorithm that successfully recovers static and dynamic polarimetric reconstructions. Compared to Regularized Maximum Likelihood methods, it avoids the need for parameter surveys, and it is not limited to the number of pixels such as recently proposed multiobjective imaging algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
