Unsupervised Induction And Gamma-Ray Burst Classification

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 et al. [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 farming 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 et al. We hypothesize that systematic biases may be responsible for this variation.