RUI: Guiding Gamma-Ray Burst Classification with the KDD Process

RUI：用 KDD 过程指导伽马射线暴分类

• 批准号: 0098499
• 负责人: Jon Hakkila
• 金额: $ 13.03万
• 依托单位: College of Charleston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0098499&HistoricalAwards=false
• 关键词: RUI; Guiding; Gamma; Ray; Burst

AST 0098499HakkilaDespite tremendous scientific breakthroughs during the past two decades, gamma-ray burst (GRB) astro-physics is still in its infancy. GRBs are the most energetic events in the universe. Further understanding of the GRB phenomenon rests in part on understand-ing distinct behaviors that can be associated with physical mechanisms. Identification of distinct physical behaviors (e.g. classification) is an important component of the scientific method. However, distinct behaviors do not always indicate the presence of separate source populations. The complex, overlapping properties of GRBs are a case in point: they have long confounded efforts to subclassify their behaviors. Many identified behaviors have been shown to result from either instrumental or sampling biases. GRB classification can be better carried out using statistically and computationally rigorous approaches of Knowledge Discovery in Databases (KDD) combined with a detailed understanding of instrumental and sampling biases. Recently, there have been claims that certain attributes correlate with burst lumi-nosity. The claims, however, are based on a very limited data set of GRBs with afterglows, and on attributes that have only been defined for a small subset of bursts observed by BATSE (the Burst And Transient Source Experiment on NASA's defunct Compton Gamma-Ray Observatory). Are the correlation's between luminosity and these attributes self-consistent with observations of the large BATSE data set? Do instrumental biases play any role in these apparent correlations? All GRBs with afterglows thus far belong to the long class of GRBs; is there similar evidence for these behaviors in the short class of GRBs? Do these attributes indicate the presence of other GRB subclasses? These questions will be addressed in this project using a large GRB database, a set of well-defined and appropriate attributes, detailed knowledge of the instrument(s) from which the observations are made, and KDD methodology. After this database has been produced, pattern recognition algorithms will be applied to GRB classifica-tion. Based on preliminary results, it is expected that general GRB subclasses will be identified, as well as substructures indicative of specific GRB behaviors.As a part of this project, a database of complex preprocessed GRB attributes will be developed and made available via the World Wide Web. Funding for this project was provided by the NSF program for Extragalactic Astronomy & Cosmology (AST/EXC).***

AST 0098499 Hakkila尽管在过去的二十年中取得了巨大的科学突破，伽马射线暴（GRB）天体物理学仍然处于起步阶段。伽玛射线暴是宇宙中能量最大的事件。对伽玛射线暴现象的进一步理解部分取决于对与物理机制相关的不同行为的理解。识别不同的物理行为（例如分类）是科学方法的重要组成部分。然而，不同的行为并不总是表明存在单独的源种群。伽马射线暴复杂、重叠的性质就是一个很好的例子：长期以来，人们一直在努力对它们的行为进行细分。许多已确定的行为已被证明是由工具或抽样偏差。利用数据库中知识发现（KDD）的统计和计算严格的方法，结合对仪器和抽样偏差的详细了解，可以更好地进行伽马射线暴分类。最近，有人声称某些属性与爆发亮度相关。然而，这些说法是基于非常有限的带有余辉的GRB数据集，以及仅为BATSE（美国宇航局已解散的康普顿伽马射线天文台的爆发和瞬态源实验）观察到的一小部分爆发定义的属性。光度和这些属性之间的相关性是否与BATSE大数据集的观测结果自洽？工具性偏差在这些明显的相关性中发挥了作用吗？到目前为止，所有有余辉的伽玛暴都属于长伽玛暴;在短伽玛暴中是否有类似的证据证明这些行为？这些属性是否表明存在其他GRB子类？本项目将利用一个大型伽玛射线暴数据库、一组定义明确的适当属性、观测仪器的详细知识以及知识发现方法来解决这些问题。数据库建立后，将利用模式识别算法对伽玛暴进行分类。根据初步结果，预计将识别出一般GRB子类，以及指示特定GRB行为的子结构。作为该项目的一部分，将开发一个复杂预处理GRB属性的数据库，并通过万维网提供。该项目的资金由美国国家科学基金会的河外天文宇宙学计划（AST/EXC）提供。