瞬态和变量的自动概率分类
Automated Probabilistic Classification of Transients and Variables
摘要：
大型的数字化天气巡天越来越多，在这些巡天中，在多个时期对天空的大面积进行重复观测。同样，在仪器、深度、节奏、波长等方面具有不同能力的后续设施（通常是自动化或机器人）的数量也在增加，其中大部分都是针对一些特定的天体物理现象。随着检测到的瞬变事件的数量的增长，检测到的变量和瞬变的自动化概率分类变得越来越重要，使得可以最佳地使用后续设施，而没有不必要的重复工作。我们描述了一种方法，现在正在开发的原型事件分类系统，它涉及贝叶斯和机器学习分类器，自动纳入后续观察的反馈，并区分或指导后续请求。这类方法可能是未来大规模天气巡天所必需的。
英文摘要：
There is an increasing number of large, digital, synoptic sky surveys, in which repeated observations are obtained over large areas of the sky in multiple epochs. Likewise, there is a growth in the number of (often automated or robotic) follow-up facilities with varied capabilities in terms of instruments, depth, cadence, wavelengths, etc., most of which are geared toward some specific astrophysical phenomenon. As the number of detected transient events grows, an automated, probabilistic classification of the detected variables and transients becomes increasingly important, so that an optimal use can be made of follow-up facilities, without unnecessary duplication of effort. We describe a methodology now under development for a prototype event classification system; it involves Bayesian and Machine Learning classifiers, automated incorporation of feedback from follow-up observations, and discriminated or directed follow-up requests. This type of methodology may be essential for the massive synoptic sky surveys in the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
