30 research outputs found
Properties of Gamma-Ray Burst Classes
The three gamma-ray burst (GRB) classes identified by statistical clustering
analysis (Mukherjee et al. 1998) are examined using the pattern recognition
algorithm C4.5 (Quinlan 1986). Although the statistical existence of Class 3
(intermediate duration, intermediate fluence, soft) is supported, the
properties of this class do not need to arise from a distinct source
population. Class 3 properties can easily be produced from Class 1 (long, high
fluence, intermediate hardness) by a combination of measurement error,
hardness/intensity correlation, and a newly-identified BATSE bias (the fluence
duration bias). Class 2 (short, low fluence, hard) does not appear to be
related to Class 1.Comment: 5 pages, 4 imbedded figures, presented at the 5th Huntsville
Gamma-Ray Burst Symposiu
AI Gamma-Ray Burst Classification: Methodology/Preliminary Results
Artificial intelligence (AI) classifiers can be used to classify unknowns,
refine existing classification parameters, and identify/screen out ineffectual
parameters. We present an AI methodology for classifying new gamma-ray bursts,
along with some preliminary results.Comment: 5 pages, 2 postscript figures. To appear in the Fourth Huntsville
Gamma-Ray Burst Symposiu
How Sample Completeness Affects Gamma-Ray Burst Classification
Unsupervised pattern recognition algorithms support the existence of three
gamma-ray burst classes; Class I (long, large fluence bursts of intermediate
spectral hardness), Class II (short, small fluence, hard bursts), and Class III
(soft bursts of intermediate durations and fluences). The algorithms
surprisingly assign larger membership to Class III than to either of the other
two classes. A known systematic bias has been previously used to explain the
existence of Class III in terms of Class I; this bias allows the fluences and
durations of some bursts to be underestimated (Hakkila et al., ApJ 538, 165,
2000). We show that this bias primarily affects only the longest bursts and
cannot explain the bulk of the Class III properties. We resolve the question of
Class III existence by demonstrating how samples obtained using standard
trigger mechanisms fail to preserve the duration characteristics of small peak
flux bursts. Sample incompleteness is thus primarily responsible for the
existence of Class III. In order to avoid this incompleteness, we show how a
new dual timescale peak flux can be defined in terms of peak flux and fluence.
The dual timescale peak flux preserves the duration distribution of faint
bursts and correlates better with spectral hardness (and presumably redshift)
than either peak flux or fluence. The techniques presented here are generic and
have applicability to the studies of other transient events. The results also
indicate that pattern recognition algorithms are sensitive to sample
completeness; this can influence the study of large astronomical databases such
as those found in a Virtual Observatory.Comment: 29 pages, 6 figures, 3 tables, Accepted for publication in The
Astrophysical Journa