无需参数设定，自动识别伽马暴长短两类群体。
A new completely parameter-free clustering algorithm for unsupervised classification of BATSE gamma-ray bursts
- 提出完全无参数的聚类算法，避免人为设定簇数。
- 从BATSE数据中准确划分出短暴和长暴两大群体。
- 结果支持合并与坍缩星理论，适合天体物理研究者。
聚类分析是理解伽马暴（GRBs）群体模式、探索其物理起源的重要机器学习方法。当前，不同可区分群体的数量仍存在争议，尽管已有多种先进聚类方法尝试解决。这一关键未知参数需通过其他调参方式间接确定，以实现对伽马暴的有效聚类。多数现有算法得出短暴与长暴分别对应合并与坍缩星的两组物理解释，但其他统计方法却挑战了这种二元划分。目前尚未证实额外聚类的存在。为此，本文提出一种全新算法，属于‘完全无参数’聚类新范式，首次以无需任何参数设定的方式对伽马暴进行分类。该方法在BATSE样本上成功识别出短暴与长暴两大主要群体，结果与合并-坍缩星理论一致。
原文摘要 · Abstract (English)
Cluster analysis is a widely applied machine learning technique to understand the existing patterns in the population of gamma-ray bursts (GRBs), in order to explore their physical sources. In the present scenario, the number of clusters corresponding to differentiable groups is still under conflict, in spite of numerous attempts with the state-of-the-art clustering procedures. This crucial unknown parameter needs to be evaluated, either directly or indirectly in terms of other tuning parameters, to produce the clusters in GRBs through implementation of an appropriate clustering algorithm. While most of the applied algorithms reached two physically explained groups of merger and collapsar predominated by the short and long bursts respectively, other statistical approaches violated this binary partition. However, physical establishment of any additional cluster(s) is not yet confirmed. Therefore, we propose a new algorithm, from a different stream of clustering referred to as `completely parameter-free', which carries out the classification of GRBs in a manner that has not been tried so far. It indicates two main groups, of short and long duration bursts from the BATSE sample, compatible with the merger-collapsar theory.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。