The Zwicky Transient Facility: Data Processing, Products, and Archive
Frank J. Masci, Russ R. Laher, Ben Rusholme, David L. Shupe, Steven Groom, Jason Surace, Edward Jackson, Serge Monkewitz, Ron Beck, David Flynn, Scott Terek, Wa… 展开作者
Frank J. Masci, Russ R. Laher, Ben Rusholme, David L. Shupe, Steven Groom, Jason Surace, Edward Jackson, Serge Monkewitz, Ron Beck, David Flynn, Scott Terek, Walter Landry, Eugean Hacopians, Vandana Desai, Justin Howell, Tim Brooke, David Imel, Stefanie Wachter, Quan-Zhi Ye, Hsing-Wen Lin, S. Bradley Cenko, Virginia Cunningham, Umaa Rebbapragada, Brian Bue, Adam A. Miller, Ashish Mahabal, Eric C. Bellm, Maria T. Patterson, Mario Jurić, V. Zach Golkhou, Eran O. Ofek, Richard Walters, Matthew Graham, Mansi M. Kasliwal, Richard G. Dekany, Thomas Kupfer, Kevin Burdge, Christopher B. Cannella, Tom Barlow, Angela Van Sistine, Matteo Giomi, Christoffer Fremling, Nadejda Blagorodnova, David Levitan, Reed Riddle, Roger M. Smith, George Helou, Thomas A. Prince, Shrinivas R. Kulkarni
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30 pages, 16 figures, Published in PASP Focus Issue on the Zwicky Transient Facility (doi: 10.1088/1538-3873/aae8ac)
The Zwicky Transient Facility (ZTF) is a new robotic time-domain survey currently in progress using the Palomar 48-inch Schmidt Telescope. ZTF uses a 47 square degree field with a 600 megapixel camera to scan the entire northern visible sky at rates of ~3760 square degrees/hour to median depths of g ~ 20.8 and r ~ 20.6 mag (AB, 5sigma in 30 sec). We describe the Science Data System that is housed at IPAC, Caltech. This comprises the data-processing pipelines, alert production system, data archive, and user interfaces for accessing and analyzing the products. The realtime pipeline employs a novel image-differencing algorithm, optimized for the detection of point source transient events. These events are vetted for reliability using a machine-learned classifier and combined with contextual information to generate data-rich alert packets. The packets become available for distribution typically within 13 minutes (95th percentile) of observation. Detected events are also linked to generate candidate moving-object tracks using a novel algorithm. Objects that move fast enough to streak in the individual exposures are also extracted and vetted. The reconstructed astrometric accuracy per science image with respect to Gaia is typically 45 to 85 milliarcsec. This is the RMS per axis on the sky for sources extracted with photometric S/N >= 10. The derived photometric precision (repeatability) at bright unsaturated fluxes varies between 8 and 25 millimag. Photometric calibration accuracy with respect to Pan-STARRS1 is generally better than 2%. The products support a broad range of scientific applications: fast and young supernovae, rare flux transients, variable stars, eclipsing binaries, variability from active galactic nuclei, counterparts to gravitational wave sources, a more complete census of Type Ia supernovae, and Solar System objects.
$\texttt{BTSbot}$: A Multi-input Convolutional Neural Network to Automate and Expedite Bright Transient Identification for the Zwicky Transient Facility
Foraging with MUSHROOMS: A Mixed-Integer Linear Programming Scheduler for Multimessenger Target of Opportunity Searches with the Zwicky Transient Facility
引力波探测的电磁跟踪是非常资源密集的，占用了几十台望远镜数小时的有限观测时间。为后续行动制定更有效的时间表将导致对应方定位效率的相应提高，而无需使用更多的望远镜时间。广泛用于运筹学和望远镜调度，混合整数线性规划（MILP）是产生这些更高效率调度的强有力的候选者，因为它可以利用强大的商业求解器，为所提供的问题找到全局最优解。我们详细介绍了一个新的目标的机会调度算法设计的Zwicky瞬态设施铭记，使用混合整数线性规划。我们比较它的性能\texttt{gwemopt}，调整启发式调度使用的Zwicky瞬态设施和其他设施在第三LIGO室女座引力波观测运行。这种新算法使用可变长度的观察块来执行节奏要求并确保现场可观察性，同时具有二次优化步骤以最小化转换时间。\blue{We show that by employing a hybrid method utilizing both this scheduler and \texttt{gwemopt}, the previous scheduler used, in concert, we can achieve an average improvement in detection efficiency of 3\%-11\% over \texttt{gwemopt} alone}的模拟二进制中子星合并数据集与LIGO-Virgo的第三次…
Tails: Chasing Comets with the Zwicky Transient Facility and Deep Learning
California Institute of Technology（加州理工学院）；IPAC, California Institute of Technology（加州理工学院IPAC）；University of Maryland（马里兰大学）；Center for Data Driven Discovery, California Institute of Technology（加州理工学院数据驱动发现中心）；University of Washington（华盛顿大学）；University of Minnesota（明尼苏达大学）；Caltech Optical Observatories, California Institute of Technology（加州理工学院光学天文台）
The Zwicky Transient Facility Bright Transient Survey. III. $\texttt{BTSbot}$: Automated Identification and Follow-up of Bright Transients with Deep Learning
Photometric classification of supernovae detected by the Zwicky Transient Facility using noise augmentation
Humboldt-Universität zu Berlin（柏林洪堡大学）；Deutsches Elektronen-Synchrotron（德国电子同步辐射加速器中心）；Leibniz-Institut für Astrophysik Potsdam (AIP)（波茨坦阿基米德天体物理研究所）；European Southern Observatory（欧洲南方天文台）；University of Washington（华盛顿大学）；Trinity College Dublin, The University of Dublin（都柏林三一学院）；California Institute of Technology（加州理工学院）；National Central University（中央大学）；Lancaster University（兰卡斯特大学）；Institute of Space Sciences (ICE, CSIC)（空间科学研究所）；Institut d’Estudis Espacials de Catalunya (IEEC)（加泰罗尼亚空间研究院）
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）
LUMOS : Linear programming Utility for Multi-messenger Optical Scheduling
Astrophysics Research Institute, Liverpool John Moores University（利物浦约翰摩尔斯大学天体物理研究所）；Department of Physics, Indian Institute of Technology Bombay（印度理工学院孟买分校物理系）；School of Physics and Astronomy, University of Minnesota（明尼苏达大学物理与天文学学院）；Astrophysics Science Division, NASA Goddard Space Flight Center（NASA戈达德太空飞行中心天体物理学科学部）；Joint Space-Science Institute, University of Maryland（马里兰大学联合空间科学研究所）
