标题： 基于无线电观测的贝叶斯推理——超越去卷积 显示英文标题

标题： Bayesian Inference for Radio Observations - Going beyond deconvolution

摘要： 射电干涉仪的数据由于天线之间的间隙而存在信息缺失的问题，这会导致图像中出现伪影，例如源周围的亮环。已经提出了多种反卷积算法来解决这一问题并生成更清晰的射电图像。然而，这些算法无法正确估计推导出的科学参数中的不确定性，也无法始终包括仪器误差的影响。我们提出了一种称为射电观测贝叶斯推理（BIRO）的替代技术，该技术使用贝叶斯统计框架同时从原始数据中确定科学参数和仪器误差，而不生成图像。我们使用了一个简单的西弗尔斯巴合成射电望远镜数据模拟，包括指向误差和波束参数作为仪器效应，以展示BIRO的应用。 显示英文摘要

摘要： Radio interferometers suffer from the problem of missing information in their data, due to the gaps between the antennas. This results in artifacts, such as bright rings around sources, in the images obtained. Multiple deconvolution algorithms have been proposed to solve this problem and produce cleaner radio images. However, these algorithms are unable to correctly estimate uncertainties in derived scientific parameters or to always include the effects of instrumental errors. We propose an alternative technique called Bayesian Inference for Radio Observations (BIRO) which uses a Bayesian statistical framework to determine the scientific parameters and instrumental errors simultaneously directly from the raw data, without making an image. We use a simple simulation of Westerbork Synthesis Radio Telescope data including pointing errors and beam parameters as instrumental effects, to demonstrate the use of BIRO.