天文瞬变的实时分类
Towards Real-time Classification of Astronomical Transients
摘要：
由于数字天气巡天的出现，时间域的探索现在是天文学研究的一个充满活力的领域。虽然全景调查可以发现可变或瞬时事件，但通常需要一些后续观察;对于短暂的现象，快速反应至关重要。随着数据率的增加，为随访研究自动分类和优先处理瞬时事件的能力变得至关重要。我们一直在开发这样的方法，使用数据流从Palomar-Quest调查，卡特琳娜巡天和其他人，使用VOEventNet框架。目标是使用新的测量，结合档案数据（先前和多波长测量）和上下文信息（例如，银河或黄道纬度，附近可能存在的宿主星系等）;并在后续数据到来时动态地对它们进行重新排序（例如，光曲线或颜色）。我们一直在研究贝叶斯方法的分类，以及歧视后续优化利用现有资源，包括朴素贝叶斯方法，和非参数高斯过程回归。我们还将部署传统机器学习技术的变体，如神经网络和支持向量机，用于建立可靠分类的瞬态数据集。
英文摘要：
Exploration of time domain is now a vibrant area of research in astronomy, driven by the advent of digital synoptic sky surveys. While panoramic surveys can detect variable or transient events, typically some follow-up observations are needed; for short-lived phenomena, a rapid response is essential. Ability to automatically classify and prioritize transient events for follow-up studies becomes critical as the data rates increase. We have been developing such methods using the data streams from the Palomar-Quest survey, the Catalina Sky Survey and others, using the VOEventNet framework. The goal is to automatically classify transient events, using the new measurements, combined with archival data (previous and multi-wavelength measurements), and contextual information (e.g., Galactic or ecliptic latitude, presence of a possible host galaxy nearby, etc.); and to iterate them dynamically as the follow-up data come in (e.g., light curves or colors). We have been investigating Bayesian methodologies for classification, as well as discriminated follow-up to optimize the use of available resources, including Naive Bayesian approach, and the non-parametric Gaussian process regression. We will also be deploying variants of the traditional machine learning techniques such as Neural Nets and Support Vector Machines on datasets of reliably classified transients as they build up.
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。
