Mining Gamma-Ray Burst Data

Abstract.

Gamma-ray bursts provide what is probably one of the messiest of all astrophysical data sets. Burst class properties are indistinct, as overlap** characteristics of individual bursts are convolved with effects of instrumental and sampling biases. Despite these complexities, data mining techniques have allowed new insights to be made about gamma-ray burst data. We demonstrate how data mining techniques have simultaneously allowed us to learn about gamma-ray burst detectors and data collection, cosmological effects in burst data, and properties of burst subclasses. We discuss the exciting future of this field, and the web-based tool we are develo** (with support from the NASA AISR Program). We invite others to join us in AI-guided gamma-ray burst classification (http://grb.mnsu.edu/grb/).