C. Hoischen, M. Füßling, S. Ohm, A. Balzer, H. Ashkar, K. Bernlöhr, P. Hofverberg, T. L. Holch, T. Murach, H. Prokoph, F. Schüssler, S. J. Zhu, D. Berge, K. Egberts, C. Stegmann
备注：
18 pages, 10 figures, accepted for publication in A&A
Observations of astrophysical transients have brought many novel discoveries and provided new insights into physical processes at work under extreme conditions in the Universe. Multi-wavelength and multi-messenger observations of variable objects require dedicated procedures and follow-up systems capable of digesting and reacting to external alerts to execute coordinated follow-up campaigns. The main functions of such follow-up systems are the processing, filtering, and ranking of the incoming alerts, the fully automated rapid execution of the observations according to an observation strategy tailored to the instrument, and real-time data analysis with feedback to the operators and other instruments. H.E.S.S. has been searching for transient phenomena since its inauguration in 2003. In this paper, we describe the transients follow-up system of H.E.S.S. which became operational in 2016. The system allows H.E.S.S. to conduct a more versatile, optimised, and largely autonomous transient follow-up program, combining all major functionalities in one systematic approach. We describe the design, central functionalities, and interfaces of the follow-up system in general and its three main components in detail: the Target of Opportunity (ToO) alert system, the data acquisition and central control system, and the real-time analysis. We highlight architectural decisions and features that enable fully automatic ToO follow-up and indicate key performance metrics of the sub-systems. We discuss the system's capabilities and highlight the need for a fine-tuned interplay of the different sub-systems in order to react quickly and reliably. Lessons learned from the development, integration, and operation of the follow-up system are reviewed in light of new and large science infrastructures and associated challenges in this exciting new era of inter-operable astronomy.
Anomaly Hunter for Alerts (AHA): Anomaly Detection in the ZTF Transient Alert Stream
Breakthrough Listen, Department of Physics, University of Oxford（牛津大学物理系突破聆听项目）；Department of Physics, University of Oxford（牛津大学物理系）；SETI Institute（SETI研究所）；Berkeley SETI Research Center, University of California, Berkeley（加州大学伯克利分校伯克利SETI研究中心）；Astrophysics Research Centre, School of Mathematics and Physics, Queen’s University Belfast（贝尔法斯特女王大学数学与物理学院天体物理研究中心）
我们介绍了4 Pi Sky 'hub'，这是一个开放式数据服务和底层软件包的集合，用于快速，全自动报告和响应天文瞬时警报。这些包建立在成熟的“VOEvent”标准化消息格式之上，旨在提供一个分散和开放的基础设施来处理瞬时警报。特别是我们提请注意voeventdb的初始版本，这是一个存档和远程查询服务，允许天文学家对瞬时警报进行历史查询。通过使用空间过滤器和引文网络查找，voeventdb能够交叉匹配瞬时警报，将多个来源的数据汇集在一起，并在规划新的后续活动时提供参考点。我们还强调了最近从ASASSN和GAIA项目添加到我们的VOEvent分发流的光学瞬态馈送。实现这些服务的源代码和部署脚本都是免费提供的，并得到许可，目的是其他团队可以使用它们来实现本地或特定于项目的VOEvent存档。在描述这些软件包的过程中，我们提供了一个基本的入门知识，包括对VOEvent标准的简要介绍。
SNGuess: A method for the selection of young extragalactic transients
Humboldt-Universität zu Berlin（柏林洪堡大学）；Stockholm University（斯德哥尔摩大学）；California Institute of Technology（加州理工学院）；Deutsches Elektronen-Synchrotron（德国电子同步加速器研究所）
Toward Autonomous Radio Follow-up of Multi-messenger Transients with RADAR: From Alert Parsing to Inference and Observation Scheduling
The University of Chicago（芝加哥大学）；Argonne National Laboratory（阿贡国家实验室）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Fink, a new generation of broker for the LSST community
Fink是一个经纪人，旨在使科学与大型时域警报流，如即将到来的Vera C。Rubin Observatory Legacy Survey of Space and Time（鲁宾天文台时空遗产巡天）它展示了传统的天文学代理功能，例如自动摄取，注释，选择和重新分配瞬变科学的有前途的警报。它还旨在通过提供实时瞬态分类来超越传统的经纪人功能，并通过使用最先进的深度学习和自适应学习技术不断改进。这些不断发展的附加值将使LSST光度数据在各种科学案例中的科学输出更加准确，同时也会导致伴随调查演变的新发现的发生率更高。在本文中，我们介绍Fink，它的科学动机，架构和当前状态，包括使用Zwicky Transient Facility警报流的第一个科学验证案例。
A Broker Integrated Algorithm for Gravitational Wave - Electromagnetic Counterpart Searches in O4a and O4b Runs
Universidad de Chile（智利大学）；Data and Artificial Intelligence Initiative (IDIA), Faculty of Physical and Mathematical Sciences, Universidad de Chile（智利大学物理与数学科学学院数据与人工智能倡议(IDIA)）；Centro de Excelencia en Astrofisica y Technologia Afines（天体物理学及相关技术卓越中心）；Millennium Institute of Astrophysics（天体物理学千年研究所）；Center for Mathematical Modeling, Universidad de Chile（智利大学数学建模中心）；Physik-Institut, Universität Zürich（苏黎世大学物理研究所）；Pontificia Universidad Católica de Chile（智利天主教 Pontificia 大学）
RAPID: Early Classification of Explosive Transients using Deep Learning
University of Cambridge（剑桥大学）；Space Telescope Science Institute（太空望远镜科学研究所）；Kavli Institute for Cosmology（卡夫利宇宙学研究所）；Stockholm University（斯德哥尔摩大学）；University of Toronto（多伦多大学）
A classification algorithm for time-domain novelties in preparation for LSST alerts: Application to variable stars and transients detected with DECam in the Galactic Bulge
Leveraging Multimodality for Real-Time Classification of Transients and Variables found by the Zwicky Transient Facility
Department of Physics and Astronomy, Northwestern University（西北大学物理与天文学系）；Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA)（天体物理跨学科探索与研究中心）；NSF-Simons AI Institute for the Sky (SkAI)（NSF-西蒙斯天空人工智能研究所）；School of Physics and Astronomy, University of Minnesota（明尼苏达大学物理与天文学学院）；Division of Physics, Mathematics, and Astronomy, California Institute of Technology（加州理工学院物理、数学与天文学部）
Alert Chain and Observation Planning for Ground Wide Angle Camera Network
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文学与空间科学学院）
Scheduling follow-up observations of energetic transients with the neutrino target scheduler (NuTS)
Colorado School of Mines（科罗拉多矿业学院）；Laboratoire Univers et Particules de Montpellier（蒙彼利埃宇宙与粒子实验室）；University of Chicago（芝加哥大学）；University of Iowa（爱荷华大学）；NASA Goddard Space Flight Center（美国宇航局戈达德太空飞行中心）；Columbia University（哥伦比亚大学）
