Despite being the most energetic electromagnetic explosions in the universe, gamma-ray bursts (GRBs) are still poorly understood. The literature recognizes two potentially different types of GRB progenitors, although statistical data suggest the existence of three GRB classes. Reliable inference of GRB physics depends on the identification of appropriate classification attributes, as well as on the statistical classification techniques used. It has recently been shown that pulses are the basic unit of GRB emission. We use new data describing GRB pulse characteristics, in conjunction with data mining tools, to provide a more reliable gamma-ray burst classification system and place additional constraints on GRB physics. We demonstrate that fewer pulses are needed to describe GRB emission than has been suggested by previous analyses, and find pulse duration to be one of the greatest delineators between GRB classes.
McAfee, Stanley and Hakkila, Jon
"Gamma-Ray Burst Classification: New Insights from Mining Pulse Data,"
Journal of the South Carolina Academy of Science: Vol. 16
, Article 6.
Available at: https://scholarcommons.sc.edu/jscas/vol16/iss1/6