We present a real-time stamp classifier of astronomical events for the ALeRCE
(Automatic Learning for the Rapid Classification of Events) broker. The
classifier is based on a convolutional neural network, trained on alerts
ingested from the Zwicky Transient Facility (ZTF). Using only the
\textit{science, reference} and \textit{difference} images of the first
detection as inputs, along with the metadata of the alert as features, the
classifier is able to correctly classify alerts from active galactic nuclei,
supernovae (SNe), variable stars, asteroids and bogus classes, with high
accuracy ($\sim$94\%) in a balanced test set. In order to find and analyze SN
candidates selected by our classifier from the ZTF alert stream, we designed
and deployed a visualization tool called SN Hunter, where relevant information
about each possible SN is displayed for the experts to choose among candidates
to report to the Transient Name Server database. From June 26th 2019 to
February 28th 2021, we have reported 6846 SN candidates to date (11.8
candidates per day on average), of which 971 have been confirmed
spectroscopically. Our ability to report objects using only a single detection
means that 70\% of the reported SNe occurred within one day after the first
detection. ALeRCE has only reported candidates not otherwise detected or
selected by other groups, therefore adding new early transients to the bulk of
objects available for early follow-up. Our work represents an important
milestone toward rapid alert classifications with the next generation of large
etendue telescopes, such as the Vera C. Rubin Observatory.

本文介绍了基于卷积神经网络的实时邮戳分类器，用于 ALERCE 代理商的天文事件分类。使用科学、参考和差异图像以及警报元数据作为特征，可将活动星系核、超新星、变量星、小行星和假分类正确分类。同时，作者还设计并部署了可以显示有关每个可能超新星的相关信息的可视化工具 SN Hunter，该工具可用于报告 ZTF 警报流中的超新星候选对象，有助于快速警报分类，为大型望远镜的下一代提供了重要的里程碑。

ALeRCE 经纪系统的警报分类：实时戳分类器

Alert Classification for the ALeRCE Broker System: The Real-time Stamp  Classifier

The unprecedented volume and rate of transient events that will be discovered
by the Large Synoptic Survey Telescope (LSST) demands that the astronomical
community update its followup paradigm. Alert-brokers -- automated software
system to sift through, characterize, annotate and prioritize events for
followup -- will be critical tools for managing alert streams in the LSST era.
The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is
one such broker. In this work, we develop a machine learning pipeline to
characterize and classify variable and transient sources only using the
available multiband optical photometry. We describe three illustrative stages
of the pipeline, serving the three goals of early, intermediate and
retrospective classification of alerts. The first takes the form of variable vs
transient categorization, the second, a multi-class typing of the combined
variable and transient dataset, and the third, a purity-driven subtyping of a
transient class. While several similar algorithms have proven themselves in
simulations, we validate their performance on real observations for the first
time. We quantitatively evaluate our pipeline on sparse, unevenly sampled,
heteroskedastic data from various existing observational campaigns, and
demonstrate very competitive classification performance. We describe our
progress towards adapting the pipeline developed in this work into a real-time
broker working on live alert streams from time-domain surveys.

本文介绍了一种机器学习方法，采用可用的光学多波段光度测量特征对宇宙学事件进行分类、表征和优先级排序，该方法适用于实时监听和处理大型天文调查过程中产生的异常事件流。