In this work, we propose a deep learning-based classification model of
astronomical objects using alerts reported by the Zwicky Transient Facility
(ZTF) survey. The model takes as inputs sequences of stamp images and metadata
contained in each alert, as well as features from the All-WISE catalog. The
proposed model, called temporal stamp classifier, is able to discriminate
between three classes of astronomical objects: Active Galactic Nuclei (AGN),
Super-Novae (SNe) and Variable Stars (VS), with an accuracy of approximately
98% in the test set, when using 2 to 5 detections. The results show that the
model performance improves with the addition of more detections. Simple
recurrence models obtain competitive results with those of more complex models
such as LSTM.We also propose changes to the original stamp classifier model,
which only uses the first detection. The performance of the latter model
improves with changes in the architecture and the addition of random rotations,
achieving a 1.46% increase in test accuracy.

通过使用 Zwicky Transient Facility（ZTF）调查报告的警报，本研究提出了一种基于深度学习的天体对象分类模型。该模型通过使用每个警报中包含的戳图像和元数据以及 All-WISE 目录的特征作为输入，能够识别出三类天体对象：活跃星系核（AGN）、超新星（SNe）和变星（VS）。在 2 到 5 次检测时，该模型在测试集中的准确率约为 98%。通过对原始戳分类器模型进行结构改变和添加随机旋转，后者的性能得到了改善，在测试准确率上增加了 1.46%。

临时时间戳分类器：分类天文预警的短序列

Temporal Stamp Classifier: Classifying Short Sequences of Astronomical  Alerts

We present a public catalog of transients from the Zwicky Transient Facility
(ZTF) Bright Transient Survey (BTS), a magnitude-limited (m<19 mag in either
the g or r filter) survey for extragalactic transients in the ZTF public
stream. We introduce cuts on survey coverage, sky visibility around peak light,
and other properties unconnected to the nature of the transient, and show that
the resulting statistical sample is spectroscopically 97% complete at <18 mag,
93% complete at <18.5 mag, and 75% complete at <19 mag. We summarize the
fundamental properties of this population, identifying distinct
duration-luminosity correlations in a variety of supernova (SN) classes and
associating the majority of fast optical transients with well-established
spectroscopic SN types (primarily SN Ibn and II/IIb). We measure the Type Ia SN
and core-collapse (CC) SN rates and luminosity functions, which show good
consistency with recent work. About 7% of CC SNe explode in very low-luminosity
galaxies (M_i > -16 mag), 10% in red-sequence galaxies, and 1% in massive
ellipticals. We find no significant difference in the luminosity or color
distributions between the host galaxies of Type II and Type Ib/c supernovae,
suggesting that line-driven wind stripping does not play a major role in the
loss of the hydrogen envelope from their progenitors. Future large-scale
classification efforts with ZTF and other wide-area surveys will provide
high-quality measurements of the rates, properties, and environments of all
known types of optical transients and limits on the existence of theoretically
predicted but as of yet unobserved explosions.

在这篇研究中，我们介绍了 ZTF Bright Transient Survey 公共目录，这是一项针对 ZTF 公共流域的限幅（在 g 或 r 滤波器中 m <19 mag）的外星暴变巡查。我们对调查范围、峰值光的天空可见性和其他与瞬变本质无关的属性进行了筛选，并展示了所得到的统计样本，在 < 18 mag 时，光谱学上的完成度为 97％，在 < 18.5 mag 时完成度为 93％，在 < 19 mag 时完成度为 75％。我们总结了这个人群的基本属性，确定了各种超新星类别中不同的持续时间 - 亮度相关性，并将大多数快速光学瞬变与已知光谱超新星类型（主要是 SN Ibn 和 II/IIb）联系起来。我们测量了 Ia 型和核心坍缩（CC）SN 的速率和亮度函数，发现它们与最近的研究基本一致。大约有 7％的 CC SNe 在非常低亮度的星系（M_i>-16 mag），10％在红序列星系中，1％在大质量椭圆体中爆炸。我们没有发现 Type II 和 Type Ib/c 超新星宿主星系之间的亮度或颜色分布存在显着差异，这表明线驱动风剥离不是它们的祖先中氢包层丢失的主要原因。未来 ZTF 和其他广域面扫描的大规模分类工作将提供所有已知类型的光学瞬变的速率、属性和环境的高质量测量，以及对理论上预测但尚未观测到的爆炸存在性的限制。

Zwicky 瞬变设施明亮瞬变调查。II. 一个公开的统计样本，用于探索超新星人口统计学

The Zwicky Transient Facility Bright Transient Survey. II. A Public  Statistical Sample for Exploring Supernova Demographics

Efficient automated detection of flux-transient, reoccurring flux-variable,
and moving objects is increasingly important for large-scale astronomical
surveys. We present braai, a convolutional-neural-network, deep-learning
real/bogus classifier designed to separate genuine astrophysical events and
objects from false positive, or bogus, detections in the data of the Zwicky
Transient Facility (ZTF), a new robotic time-domain survey currently in
operation at the Palomar Observatory in California, USA. Braai demonstrates a
state-of-the-art performance as quantified by its low false negative and false
positive rates. We describe the open-source software tools used internally at
Caltech to archive and access ZTF's alerts and light curves (Kowalski), and to
label the data (Zwickyverse). We also report the initial results of the
classifier deployment on the Edge Tensor Processing Units (TPUs) that show
comparable performance in terms of accuracy, but in a much more (cost-)
efficient manner, which has significant implications for current and future
surveys.

本文介绍了一种卷积神经网络深度学习算法（braai），用于识别天文学巡天过程中由 Zwicky 瞬变观测设备产生的真实物理事件和对象，开发者还描述了用于存档和访问 ZTF 警报和光度曲线的开源软件工具，以及用于标记数据的 Zwickyverse。同时报道了分类器部署在边缘张量处理单元（TPUs）上的初步结果展示了与准确度相抵触的更（成本）高效的状态 - 最先进的性能，这对当前和未来市场调查具有重要的影响。

利用深度学习对 Zwicky 瞬变设施进行真假分类

Real-bogus classification for the Zwicky Transient Facility using deep  learning

We present DeepStreaks, a convolutional-neural-network, deep-learning system
designed to efficiently identify streaking fast-moving near-Earth objects that
are detected in the data of the Zwicky Transient Facility (ZTF), a wide-field,
time-domain survey using a dedicated 47 sq. deg camera attached to the Samuel
Oschin 48-inch Telescope at the Palomar Observatory in California, United
States. The system demonstrates a 96-98% true positive rate, depending on the
night, while keeping the false positive rate below 1%. The sensitivity of
DeepStreaks is quantified by the performance on the test data sets as well as
using known near-Earth objects observed by ZTF. The system is deployed and
adapted for usage within the ZTF Solar-System framework and has significantly
reduced human involvement in the streak identification process, from several
hours to typically under 10 minutes per day.

使用卷积神经网络的 DeepStreaks 系统能够高效、准确地识别和追踪 ZTF 所捕获的高速行星，有效减少人工识别的时间和准确性。

DeepStreaks：使用深度学习在 Zwicky 瞬变设施数据中识别快速运动的物体

DeepStreaks: identifying fast-moving objects in the Zwicky Transient  Facility data with deep learning

Both multi-messenger astronomy and new high-throughput wide-field surveys
require flexible tools for the selection and analysis of astrophysical
transients. We here introduce the Alert Management, Photometry and Evaluation
of Lightcurves (AMPEL) system, an analysis framework designed for
high-throughput surveys and suited for streamed data. AMPEL combines the
functionality of an alert broker with a generic framework capable of hosting
user-contributed code, that encourages provenance and keeps track of the
varying information states that a transient displays. The latter concept
includes information gathered over time and data policies such as access or
calibration levels.
We describe a novel ongoing real-time multi-messenger analysis using AMPEL to
combine IceCube neutrino data with the alert streams of the Zwicky Transient
Facility (ZTF). We also reprocess the first four months of ZTF public alerts,
and compare the yields of more than 200 different transient selection functions
to quantify efficiencies for selecting Type Ia supernovae that were reported to
the Transient Name Server (TNS). We highlight three channels suitable for (1)
the collection of a complete sample of extragalactic transients, (2) immediate
follow-up of nearby transients and (3) follow-up campaigns targeting young,
extragalactic transients. We confirm ZTF completeness in that all TNS
supernovae positioned on active CCD regions were detected.
AMPEL can assist in filtering transients in real time, running alert reaction
simulations, the reprocessing of full datasets as well as in the final
scientific analysis of transient data. This text introduces how users can
design their own channels for inclusion in the AMPEL live instance that parses
the ZTF stream and the real-time submission of high quality extragalactic
supernova candidates to the TNS.

介绍了 Alert Management，Photometry 和 Evaluation of Lightcurves（AMPEL）系统用于多通道天文学与新的高通量巡天数据的处理与分析，并重点描述了将 IceCube 中微子数据与 ZTF 警报流进行多信使分析的实时应用，证实了所有 TNS 超新星的检测完整性。

使用 AMPEL 进行瞬变处理和分析：警报管理、光变曲线测光和评估

Transient processing and analysis using $\texttt{AMPEL}$: Alert  Management, Photometry and Evaluation of Lightcurves

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.

Zwicky Transient Facility (ZTF) is a time-domain survey utilizing a unique image-differencing algorithm, sensitive photometry, and data-rich alert packets to detect and track variable sources of all varieties, in real time, for a wide range of scientific applications.

Zwicky 瞬变设施：数据处理、产品和档案

The Zwicky Transient Facility: Data Processing, Products, and Archive

The Zwicky Transient Facility (ZTF) is a new optical time-domain survey that
uses the Palomar 48-inch Schmidt telescope. A custom-built wide-field camera
provides a 47 deg$^2$ field of view and 8 second readout time, yielding more
than an order of magnitude improvement in survey speed relative to its
predecessor survey, the Palomar Transient Factory (PTF). We describe the design
and implementation of the camera and observing system. The ZTF data system at
the Infrared Processing and Analysis Center provides near-real-time reduction
to identify moving and varying objects. We outline the analysis pipelines, data
products, and associated archive. Finally, we present on-sky performance
analysis and first scientific results from commissioning and the early survey.
ZTF's public alert stream will serve as a useful precursor for that of the
Large Synoptic Survey Telescope.

Zwicky Transient Facility (ZTF) 是一项新的光学时域巡天，使用 Palomar 48 英寸 Schmidt 望远镜，所采用的自定义广角相机提供了 47 平方度的视场和 8 秒的读出时间，相对于其前身巡天 Palomar Transient Factory（PTF），提高了一个数量级的观测速度。该研究描述了相机和观测系统的设计和实现，以及数据处理和分析方案，最终给出了 ZTF 的初步科学结果和性能分析。

ZTF 瞬变天文台：系统概述、性能和首批结果

The Zwicky Transient Facility: System Overview, Performance, and First  Results

Near-Earth asteroids (NEAs) in the 1-100 meter size range are estimated to be
$\sim$1,000 times more numerous than the $\sim$15,000 currently-catalogued
NEAs, most of which are in the 0.5-10 kilometer size range. Impacts from 10-100
meter size NEAs are not statistically life-threatening but may cause
significant regional damage, while 1-10 meter size NEAs with low velocities
relative to Earth are compelling targets for space missions. We describe the
implementation and initial results of a real-time NEA-discovery system
specialized for the detection of small, high angular rate (visually-streaked)
NEAs in Palomar Transient Factory (PTF) images. PTF is a 1.2-m aperture,
7.3-deg$^2$ field-of-view optical survey designed primarily for the discovery
of extragalactic transients (e.g., supernovae) in 60-second exposures reaching
$\sim$20.5 visual magnitude. Our real-time NEA discovery pipeline uses a
machine-learned classifier to filter a large number of false-positive streak
detections, permitting a human scanner to efficiently and remotely identify
real asteroid streaks during the night. Upon recognition of a streaked NEA
detection (typically within an hour of the discovery exposure), the scanner
triggers follow-up with the same telescope and posts the observations to the
Minor Planet Center for worldwide confirmation. We describe our ten initial
confirmed discoveries, all small NEAs that passed 0.3-15 lunar distances from
Earth. Lastly, we derive useful scaling laws for comparing
streaked-NEA-detection capabilities of different surveys as a function of their
hardware and survey-pattern characteristics. This work most directly informs
estimates of the streak-detection capabilities of the Zwicky Transient Facility
(ZTF, planned to succeed PTF in 2017), which will apply PTF's current
resolution and sensitivity over a 47-deg$^2$ field-of-view.

这篇论文采用机器学习的方法实时发现小型、高角速度的近地小行星，并研究了如何比较不同观测系统对高角速度行星的观测能力，为 Zwicky Transient Facility 的建设提供了参考。