Unsupervised induction and gamma-ray burst classification
作者:
Richard J. Roiger,
Jon Hakkila,
David J. Haglin,
Geoffrey N. Pendleton,
Robert S. Mallozzi,
期刊:
AIP Conference Proceedings
(AIP Available online 1900)
卷期:
Volume 526,
issue 1
页码: 38-42
ISSN:0094-243X
年代: 1900
DOI:10.1063/1.1361503
出版商: AIP
数据来源: AIP
摘要:
We use ESX, a product of Information Acumen Corporation, to perform unsupervised learning on a data set containing 797 gamma-ray bursts taken from the BATSE 3B catalog [5]. Assuming all attributes to be distributed log-normally, Mukherjee &etal; [6] analyzed these same data using a statistical cluster analysis. Utilizing the logarithmic values for T90 duration, total fluence, and hardness ratio HR321 their results showed the instances formed three classes. Class I contained long/bright/intermediate bursts, class II consisted of short/faint/hard bursts and class III was represented by intermediate/intermediate/soft bursts. When ESX was presented with these data and restricted to forming a small number of classes, the two classes found by previous standard techniques [1] were determined. However, when ESX was allowed to form more than two classes, four classes were created. One of the four classes contained a majority of short bursts, a second class consisted of mostly intermediate bursts, and the final two classes were subsets of the Class I (long) bursts determined by Mukherjee &etal; We hypothesize that systematic biases may be responsible for this variation. ©2000 American Institute of Physics.
点击下载:
PDF
(1483KB)
返 回