How Sample Completeness Affects Gamma-Ray Burst Classification

@article{Hakkila2003HowSC,
  title={How Sample Completeness Affects Gamma-Ray Burst Classification},
  author={Jon Hakkila and Timothy W. Giblin and Richard J. Roiger and David J. Haglin and W. S. Paciesas and Charles A. Meegan},
  journal={The Astrophysical Journal},
  year={2003},
  volume={582},
  pages={320-329}
}
Unsupervised pattern-recognition algorithms support the existence of three gamma-ray burst classes: class 1 (long, large-fluence bursts of intermediate spectral hardness), class 2 (short, small-fluence, hard bursts), and class 3 (soft bursts of intermediate durations and fluences). The algorithms surprisingly assign larger membership to class 3 than to either of the other two classes. A known systematic bias has been previously used to explain the existence of class 3 in terms of class 1; this… Expand

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