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

尽管在过去的二十年里取得了巨大的科学突破，伽马射线暴(GRB)天文物理学仍处于初级阶段。伽玛暴是宇宙中能量最大的事件。对伽玛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不同的源种群。伽玛暴复杂、重叠的特性就是一个很好的例子：长期以来，它们一直在混淆对其行为进行细分的努力。许多已被识别的行为已被证明是工具或抽样偏差的结果。使用数据库中严格的统计和计算知识发现(KDD)方法，结合对仪器和抽样偏差的详细了解，可以更好地执行GRB分类。最近，有人声称某些属性与突发性发光性有关。然而，这些说法是基于非常有限的具有余辉的伽玛暴数据集，以及只为BATSE(NASA已停产的康普顿伽马射线天文台上的爆发和瞬变源实验)观测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与大型BATSE数据集的观测结果是否自洽？工具性偏见在这些明显的相关性中扮演了什么角色？到目前为止，所有有余辉的伽玛暴都属于长类型的伽玛暴；在短类型的伽玛暴中是否有类似的证据证明这些行为？这些属性是否表示存在其他GRB子类？这些问题将在本项目中使用大型伽马射线暴数据库、一套定义明确和适当的属性、进行观测的仪器(S)的详细知识和知识发现方法来解决。该数据库建立后，模式识别算法将应用于伽玛暴的分类。根据初步结果，预计将确定一般的GRB子类，以及表示特定GRB行为的子结构。作为该项目的一部分，将开发一个复杂的经过预处理的GRB属性数据库，并通过万维网提供。该项目的资金由NSF河外天文与宇宙学计划(AST/EXC)提供。*

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).***