Rubin时代天文时间域观测中光学瞬变发现与分类的自动化
The automation of optical transient discovery and classification in Rubin-era time-domain astronomy
AI总结：
本文探讨了Rubin时代光学时间域天文学中瞬变发现与分类的自动化发展，分析了现有工作流程的现状，并提出了加速自动化的建议。
AI中文摘要：
像Zwicky瞬变设施和小行星地球撞击最后警报系统这样的机器人宽视场时间域调查，每晚都能捕捉到数十个瞬变现象。在数十年的发展过程中，发现和分类瞬变现象的流程在调查数据流中已经变得越来越自动化。最近机器学习和人工智能工具的整合产生了重大里程碑，包括实现了光学瞬变的全自动端到端发现和分类，以及实现了自动化快速响应的空间后续观测。现已投入运营的Vera C. Rubin天文台及其空间与时间遗产调查，正在加速瞬变现象的发现速度，并以惊人的速度产生大量数据。鉴于预期的瞬变发现数量将增加一个数量级，光学时间域天文学的一个有前景的发展方向是加大对自动化流程的投入。本文回顾了当前实时瞬变工作流程的范式，预测Rubin时代期间其演变，并提出加快瞬变天文学自动化的建议。
英文摘要：
Robotic wide-field time-domain surveys, such as the Zwicky Transient Facility and the Asteroid Terrestrial-impact Last Alert System, capture dozens of transients each night. The workflows for discovering and classifying transients in survey data streams have become increasingly automated over decades of development. The recent integration of machine learning and artificial intelligence tools has produced major milestones, including the fully automated end-to-end discovery and classification of an optical transient, and has enabled automated rapid-response space-based follow-up. The now-operational Vera C. Rubin Observatory and its Legacy Survey of Space and Time are accelerating the rate of transient discovery and producing large volumes of data at incredible rates. Given the expected order-of-magnitude increase in transient discoveries, one promising path forwards for optical time-domain astronomy is heavily investing in accelerating the automation of our workflows. Here we review the current paradigm of real-time transient workflows, project their evolution during the Rubin era and present recommendations for accelerating transient astronomy with automation.
