With the upcoming commensal surveys for Fast Radio Bursts (FRBs), and their high candidate rate, usage of machine learning algorithms for candidate classification is a necessity. Such algorithms will also play a pivotal role in sending real-time triggers for prompt follow-ups with other instruments. In this paper, we have used the technique of Transfer Learning to train the state-of-the-art deep neural networks for classification of FRB and Radio Frequency Interference (RFI) candidates. These are convolutional neural networks which work on radio frequency-time and dispersion measure-time images as the inputs. We trained these networks using simulated FRBs and real RFI candidates from telescopes at the Green Bank Observatory. We present 11 deep learning models, each with an accuracy and recall above 99.5% on our test dataset comprising of real RFI and pulsar candidates. As we demonstrate, these algorithms are telescope and frequency agnostic and are able to detect all FRBs with signal-to-noise ratios above 10 in ASKAP and Parkes data. We also provide an open-source python package FETCH (Fast Extragalactic Transient Candidate Hunter) for classification of candidates, using our models. Using FETCH, these models can be deployed along with any commensal search pipeline for real-time candidate classification.
Transfer learning for transient search with small-field optical survey telescopes
Aryabhatta Research Institute of Observational Sciences (ARIES)（阿亚帕塔观测科学研究所）；Department of Applied Optics and Photonics University of Calcutta（加尔各答大学应用光学与光子学系）；Institute of Astrophysics and Geophysics University of Liège（列日大学天体物理学与地球物理研究所）；Department of Physics Loyola College（罗耀拉学院物理系）；Department of Physics School of Advanced Sciences Vellore Institute of Technology（维洛尔理工大学高级科学学院物理系）
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients
Irvington High School（欧文顿高中）；University of the Western Cape（西开普大学）；South African Radio Astronomy Observatory（南非射电天文观测台）；Massachusetts Institute of Technology（麻省理工学院）
AppleCiDEr. II. SpectraNet: A Spectroscopic Neural Network Classifier for Transients Demonstrated on ZTF Follow-up Data
University of Minnesota（明尼苏达大学）；California Institute of Technology（加州理工学院）；Northwestern University（西北大学）；Stockholm University（斯德哥尔摩大学）；Drexel University（德雷塞尔大学）
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）
Investigating the Effects of Point Source Injection Strategies on KMTNet Real/Bogus Classification
Pohang University of Science and Technology (POSTECH)（浦项科技大学）；Institute for Astronomy, University of Hawaii（夏威夷大学天体物理研究所）；Seoul National University（首尔国立大学）
A GPU-Accelerated Transient Detection Pipeline for DECam Time-Domain Surveys
Carnegie Mellon University（卡内基梅隆大学）；University of Pennsylvania（宾夕法尼亚大学）；Texas A. & M. University（德克萨斯农工大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Alert Classification for the ALeRCE Broker System: The Light Curve Classifier
我们提出了第一个版本的ALERCE（自动学习的快速分类事件）经纪人光曲线分类器。ALERCE目前正在处理Zwicky瞬态设施（ZTF）警报流，为Vera C做准备。鲁宾天文台。ALERCE光变曲线分类器使用从ZTF警报流计算的可变性特征，以及从AllWISE和ZTF测光获得的颜色。我们应用一个平衡的随机森林算法与两个级别的计划，其中顶层分类每个源周期性的，随机的，或瞬态的，和底层进一步解决这些分层类中的每一个，在15个总类。这个分类器对应于第一次尝试分类多个类别的随机变量（包括核心和主机为主的活动星系核，耀变体，年轻的恒星物体，和灾难性的变量），除了不同类别的周期性和瞬态源，使用真实的数据。我们使用各种公共星表（如卡塔利娜巡天和{ \em Gaia} DR 2变星星表，以及百万类星体星表）创建了一个标记集，并对ZTF中具有$\geq6$ $g$波段或$\geq6$ $r$波段探测的所有对象进行分类（截至2020/06/09共有868，371个源），每天提供新警报源的更新分类。对于顶层，我们得到的宏观平均精度和召回率得分分别为0.96和0.99，而对于底层，我们得到的宏观平均精度和召回率得分分别为0.57和0.76。来自光变曲线分类器的更新分类可在\href{http://alerce.online}{ALeRCE Explorer website}中找到。
LUNCH: A Lightweight Unified Deep-Learning Framework for General Transients Classification in High-Energy Time-Domain Astronomy
College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；State Key Laboratory of Particle Astrophysics, Institute of High Energy Physics, Chinese Academy of Sciences（中国科学院高能物理研究所粒子天体物理国家重点实验室）
TTC: Transformer-based TDE Classifier for the Wide Field Survey Telescope (WFST)
University of Science and Technology of China（中国科学技术大学）；Institute of Deep Space Sciences, Deep Space Exploration Laboratory（深空探测实验室深空科学研究所）；National Astronomical Observatory of Japan, National Institutes of Natural Sciences（日本国立自然科学研究所国立天文台）；State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China（中国科学技术大学粒子探测电子学国家重点实验室）；National Optical Astronomy Observatory (NSF’s National Optical-Infrared Astronomy Research Laboratory)（美国国家光学天文台（美国国家科学基金会国家光红外天文研究实验室））；Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）；Institute of Optics and Electronics, Chinese Academy of Sciences（中国科学院光电技术研究所）；Tsung-Dao Lee Institute and Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education, Shanghai Jiao Tong University（上海交通大学教育部粒子物理、天体物理与宇宙学重点实验室及汤川中心）
First fast radio burst search campaign at the Argentine Institute of Radio Astronomy
Rochester Institute of Technology（罗切斯特理工学院）；Instituto Argentino de Radioastronomía (CCT La Plata, CONICET（阿根廷射电天文研究所）；CICPBA（科尔多瓦大学数学、天文学、物理与计算学院）；UNLP)（理论与实验天文学研究所）；Facultad de Matemática, Astronomía, Física y Computación, UNC（马克斯·普朗克引力物理学研究所（阿尔伯特·爱因斯坦研究所））；Instituto de Astronomía Teórica y Experimental, CONICET-UNC；Max Planck Institute for Gravitational Physics (Albert Einstein Institute)
Classifying Image Sequences of Astronomical Transients with Deep Neural Networks
Universidad de los Andes（洛斯安第斯大学）；Center for Research and Formation in Artificial Intelligence（人工智能研究与培训中心）；Systems and Computing Engineering Department（系统与计算工程系）；Departamento de Física（物理系）
Lomonosov Moscow State University（莫斯科国立大学）；National Research University Higher School of Economics（俄罗斯高等经济大学）；Ural Federal University（乌拉尔联邦大学）；Carnegie Mellon University（卡内基梅隆大学）
