# Multidimensional analysis of Fermi GBM gamma-ray bursts

## TL;DR

This study uses principal component analysis and clustering on Fermi GBM gamma-ray burst data to explore classification, confirming the existence of short and two long-duration classes, but not supporting the intermediate class.

## Contribution

The paper applies multivariate statistical techniques to a large gamma-ray burst dataset, providing insights into classification and questioning the intermediate class validity.

## Key findings

- Identifies a short class and two distinct long-duration classes.
- Spectral hardness is not a definitive property for class separation.
- Fermi GBM spectral variables are complex and not straightforward for classification.

## Abstract

The Fermi GBM catalog provides a large database with many measured variables that can be used to explore and verify gamma-ray burst classification results. We have used Principal Component Analysis and statistical clustering techniques to look for clustering in a sample of 801 gamma-ray bursts described by sixteen classification variables. The analysis recovers what appears to be the Short class and two long-duration classes that differ from one another via peak flux, with negligible variations in fluence, duration and spectral hardness. Neither class has properties entirely consistent with the Intermediate GRB class. Spectral hardness has been a critical Intermediate class property. Rather than providing spectral hardness, Fermi GBM provides a range of fitting variables for four different spectral models; it is not intuitive how these variables can be used to support or disprove previous GRB classification results.