Multidimensional analysis of Fermi GBM gamma-ray bursts

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 longduration 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.

A Tight Three-parameter Correlation and Related Classification on Gamma-Ray Bursts

“…The three subclasses classification scheme has been discussed in different aspects (Virgili et al 2009;Bhave et al 2017;Svinkin et al 2019;Horváth et al 2019). Classifying LGRBs based on hardness ratio, peak flux, or luminosity have similar conclusions as our analysis.…”

“…Svinkin et al (2019) analyzed GRBs detected by Konus-Wind with similar methods and also suggested a three-component classification. Chattopadhyay & Maitra (2017, 2018 divided GRBs into five categories by gaussian (and t-)mixture-model-based cluster analysis, and Horváth et al (2019) proposed a three-subclass GRBs classification scheme. Meanwhile, some researches (Tarnopolski 2019a,b) imply that the third class is unlikely to be a real phenomenon.…”

A Comprehensive Power Spectral Density Analysis of Astronomical Time Series. II. The Swift/BAT Long Gamma-Ray Bursts

“…t-SNE groups similar LCs close together, based on which it was demonstrated that as a result two prominent clusters emerged, each corresponding to the short and long subclasses, respectively. This appears to resolve the issue whether there are two or three main GRB types (Horváth 2002;Horváth et al 2008;Tarnopolski 2016a;Horváth et al 2019;Tóth et al 2019;Tarnopolski 2019a,b,c).…”

Graph-based clustering of gamma-ray bursts

“…Moreover, considering skewed distributions, two-component mixtures have also been indicated (Tarnopolski 2019b,a). In higher-dimensional spaces, things become less unambiguous (Mukherjee et al 1998;Chattopadhyay et al 2007;Chattopadhyay & Maitra 2017Modak et al 2018;Acuner & Ryde 2018;Horváth et al 2019;Tóth et al 2019;Tarnopolski 2019c). For the most recent, detailed overview on the topic of parametric clustering of GRBs, readers can refer to Tarnopolski (2019c).…”