Self-calibration methods with the CLEAN algorithm have been widely employed in Very Long Baseline Interferometry (VLBI) data processing in order to correct antenna-based amplitude and phase corruptions present in the data. However, human interaction during the conventional CLEAN self-calibration process can impose a strong effective prior, which in turn may produce artifacts within the final image and hinder the reproducibility of final results. In this work, we aim to demonstrate a combined self-calibration and imaging method for VLBI data in a Bayesian inference framework. The method corrects for amplitude and phase gains for each antenna and polarization mode by inferring the temporal correlation of the gain solutions. We use Stokes I data of M87 taken with the Very Long Baseline Array (VLBA) at 43GHz, pre-calibrated using the rPICARD CASA-based pipeline. For antenna-based gain calibration and imaging, we use the Bayesian imaging software resolve. To estimate gain and image uncertainties, we use a Variational Inference method. We obtain a high-resolution M87 Stokes I image at 43GHz in conjunction with antenna-based gain solutions using our Bayesian self-calibration and imaging method. The core with counter-jet structure is better resolved, and extended jet emission is better described compared to the CLEAN reconstruction. Furthermore, uncertainty estimation of the image and antenna-based gains allows us to quantify the reliability of the result. Our Bayesian self-calibration and imaging method is able to reconstruct robust and reproducible Stokes I images and gain solutions with uncertainty estimation by taking into account the uncertainty information in the data.
Bayesian polarization calibration and imaging in very long baseline interferometry
Max-Planck-Institut für Radioastronomie（马克斯·普朗克射电天文研究所）；Max Planck Computing and Data Facility（马克斯·普朗克计算与数据中心）；Max-Planck-Institut für Astrophysik（马克斯·普朗克天体物理研究所）；Technische Universität München (TUM)（慕尼黑工业大学）；School of Space Research, Kyung Hee University（庆熙大学宇宙研究学院）；Ludwig-Maximilians-Universität（路德维希-马克西米利安大学）；Department of Astrophysics, Institute for Mathematics, Astrophysics and Particle Physics (IMAPP), Radboud University（拉布德大学数学、天体物理学和粒子物理研究所天体物理学系）；Institut für Experimentalphysik（实验物理研究所）
Calibration artefacts in radio interferometry. I. Ghost sources in WSRT data
这项工作研究了一类特殊的人工制品，或鬼源，在无线电干涉图像。韦斯特博克综合射电望远镜（Westerbork Synthesis Radio Telescope，WSRT）的早期观测（和模拟）表明，这些都是由于不完整的天空模型校准造成的。一个理论框架的推导，验证了这一建议，并提供了在两个源的情况下，鬼形成的预测。预测被发现准确地匹配模拟的结果，并定性地再现了以前在观测数据中看到的鬼。该理论还提供了许多以前令人困惑的功能，这些文物（规则的几何形状，PSF样旁瓣，似乎独立于模型通量）的解释，并表明，所观察到的现象影响未建模的源通量抑制是由于相同的机制。我们证明，这种鬼形成机制是校准的一个基本特征，并表现出特别强大的和本地化的签名，由于阵列冗余。在某种程度上，这种机制将影响所有的观测（包括那些非冗余阵列），但在大多数情况下，鬼仍然隐藏在噪音或其他仪器伪影掩盖。这种错误对未来的深层观测的影响进行了讨论。
Bayesian Imaging for Radio Interferometry with Score-Based Priors
Université de Montréal（蒙特利尔大学）；Ciela Institute（谢拉研究所）；Mila（米拉研究所）；University of Manchester（曼彻斯特大学）；Flatiron Institute（熨斗研究所）；The Alan Turing Institute（艾伦·图灵研究所）；Trottier Space Institute（特罗蒂尔空间研究所）；Perimeter Institute（圆周理论物理研究所）
A Python-Based Peeling Framework for Radio Interferometry: Application to uGMRT 650MHz Imaging
Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）；University of Science and Technology of China（中国科学技术大学）；Yunnan Observatories, Chinese Academy of Sciences（中国科学院云南天文台）；Nanjing University（南京大学）；National Radio Astronomy Observatory, NRAO（美国国家射电天文台）；University of Cape Town（开普敦大学）；University of the Western Cape（西开普大学）
The CARMA Paired Antenna Calibration System: Atmospheric Phase Correction for Millimeter Wave Interferometry and its Application to Mapping the Ultraluminous Galaxy Arp 193
IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors
Université de Montréal（蒙特利尔大学）；Mila - Quebec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning（CIELA-蒙特利尔天体物理数据分析与机器学习研究所）；University of Oxford（牛津大学）；Flatiron Institute - Center for Computational Astrophysics（弗拉蒂伦研究所-计算天体物理中心）；Perimeter Institute（圆周理论物理研究所）；McGill University（麦吉尔大学）；University of Manchester（曼彻斯特大学）；The Alan Turing Institute（阿兰·图灵研究所）
本文提出贝叶斯成像框架 IRIS，将星系光学图像训练的基于分数模型先验与 uv 空间高斯似然结合，用于 ALMA DSHARP 原行星盘可见度重建，并通过模拟覆盖率测试验证后验校准。
The Bayesian Global Sky Model (B-GSM): Validation of a Data Driven Bayesian Simultaneous Component Separation and Calibration Algorithm for EoR Foreground Modelling
Institute of Astronomy, University of Cambridge（剑桥大学天文学研究所）；Cavendish Laboratory, Department of Physics, University of Cambridge（剑桥大学物理系卡文迪许实验室）
A Probabilistic Calibration Procedure for the CORSAIR Polarimeter
Harvard University（哈佛大学）；Center for Astrophysics | Harvard & Smithsonian（天体物理中心 | 哈佛与史密森尼学会）；Solar Scientific LLC（太阳科学有限责任公司）；National Solar Observatory（国家太阳天文台）；Institute for Astronomy, University of Hawaii at Manoa（夏威夷大学马诺阿分校天体物理研究所）
