面向实时瞬态分类引擎
Towards a Real-time Transient Classification Engine
摘要：
时间采样不仅仅是给可观测量的向量增加了另一个轴。相反，在认识到物体如何随时间变化（和移动）直接与天文现象背后的物理学有关的情况下，下一代广域天气调查准备彻底改变我们对夜间颠簸的任何东西（在某种程度上几乎是一切）的理解。尽管如此，即使是最雄心勃勃的调查将需要有针对性的光谱后续工作，以填补新发现的瞬变的物理细节。我们现在正在建立一个新的系统，旨在从高通量成像数据流中近实时地摄取和分类瞬态现象。在此描述的是，伯克利的瞬态分类项目将利用从时间序列和上下文（静态）信息中提取的“特征”操作的分类技术。我们还强调需要社区采用天文时间序列数据的标准表示（即，"VOTimeseries'"）。
英文摘要：
Temporal sampling does more than add another axis to the vector of observables. Instead, under the recognition that how objects change (and move) in time speaks directly to the physics underlying astronomical phenomena, next-generation wide-field synoptic surveys are poised to revolutionize our understanding of just about anything that goes bump in the night (which is just about everything at some level). Still, even the most ambitious surveys will require targeted spectroscopic follow-up to fill in the physical details of newly discovered transients. We are now building a new system intended to ingest and classify transient phenomena in near real-time from high-throughput imaging data streams. Described herein, the Transient Classification Project at Berkeley will be making use of classification techniques operating on ``features'' extracted from time series and contextual (static) information. We also highlight the need for a community adoption of a standard representation of astronomical time series data (i.e., ``VOTimeseries'').
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。
