We present VOLKS2, the second release of "VLBI Observation for transient Localization Keen Searcher". The pipeline aims at transient search in regular VLBI observations as well as detection of single pulses from known sources in dedicated VLBI observations. The underlying method takes the idea of geodetic VLBI data processing, including fringe fitting to maximize the signal power and geodetic VLBI solving for localization. By filtering the candidate signals with multiple windows within a baseline and by cross matching with multiple baselines, RFIs are eliminated effectively. Unlike the station auto spectrum based method, RFI flagging is not required in the VOLKS2 pipeline. EVN observation (EL060) is carried out, so as to verify the pipeline's detection efficiency and localization accuracy in the whole FoV. The pipeline is parallelized with MPI and further accelerated with GPU, so as to exploit the hardware resources of modern GPU clusters. We can prove that, with proper optimization, VOLKS2 could achieve comparable performance as auto spectrum based pipelines. All the code and documents are publicly available, in the hope that our pipeline is useful for radio transient studies.
VLBI Astrometric Procedure and Performance with CHIME/FRB Outriggers
Massachusetts Institute of Technology（麻省理工学院）；University of Toronto（多伦多大学）；University of California Berkeley（加州大学伯克利分校）；University of Amsterdam（阿姆斯特丹大学）
VLBI-Enabled Localization of Continuous GW Sources
Kumamoto University（熊本大学）；National Astronomical Observatory of Japan（日本国立天文台）；Yamaguchi University（山口大学）；Kagoshima University（鹿儿岛大学）；Tohoku Institute of Technology（东北工业大学）；National Astronomical Research Institute of Thailand（泰国国立天文研究所）；Tohoku University（东北大学）；Japan Aerospace Exploration Agency（日本宇宙航空研究开发机构）
Project management of the Canadian Hydrogen Observatory and Radio-transient Detector
McGill University（麦吉尔大学）；National Research Council Canada（加拿大国家研究委员会）；Perimeter Institute for Theoretical Physics（珀米特理论物理研究所）；University of Toronto（多伦多大学）
Detecting optical transients using artificial neural networks and reference images from different surveys
The University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；University of Warsaw（华沙大学）；National Centre for Nuclear Research（国家核研究中心）；Texas Tech University（德克萨斯理工大学）
