Analysis of the Duration–Hardness Ratio Plane of Gamma-Ray Bursts Using Skewed Distributions

Abstract

It was recently shown that the T 90 −H 32 distributions of gamma-ray bursts from CGRO/BATSE and Fermi/GBM are well described by a mixture of only two skewed components, making the presumed third, intermediate class unnecesary. The Swift/BAT, Konus-Wind, RHESSI and Suzaku/WAM data sets are found to be consistent with a two-class description as well.

Graph-based clustering of gamma-ray bursts

“…A much less clear picture is obtained for Fermi, for which the γ plot hints at about 15 classes, which seems highly unreliable and hence inconclusive. For Konus, it appears that clustering into three groups is appropriate; however, the division seems nonstandard, yet similar to what was obtained with the Gaussian mixture model in the case of Fermi GRBs (Tarnopolski 2019a). Swift's clustering into two groups is clearly erratic since the separation between short and long GRBs occurs at T 90 100 s. Partitioning into three classes is more sensible; however, it is unclear why the long GRBs ought to be divided also at T 90 100 s. It appears the algorithm just divides the cluster at the point of the highest local density into groups lying to the left and right of it.…”

“…Hence the parallel investigation of six GRB samples was conducted herein to paint a more complete picture. For discussions on the instrumental effects and biases, readers can refer to Shahmoradi (2013); Shahmoradi & Nemiroff (2015); Řípa & Mészáros (2016); Tarnopolski (2019a) and the references therein. Some of the employed graph-based methods applied to GRBs worked relatively well for some data sets, while they gave rather unreliable results for others.…”

“…The Burst And Transient Source Experiment 1 (BATSE) onboard the Compton Gamma-Ray Observatory observed 1954 GRBs with T 90 and H 32 , with the hardness ratio computed within slightly different energy bands: H 32 = F 100−300 keV F 50−100 keV . This sample was also analysed by Tarnopolski (2019a). Herein, 1953 GRBs are utilised as one point has both log T 90 and log H 32 below −1, and it is excluded as an obvious outlier.…”

GRB X-Ray Flare Properties among Different GRB Subclasses

“…In the literature, Tarnopolski (2015) summarized long/short classification by several GRB detectors. Tarnopolski (2019) further noted that the Swift-BAT detection is more sensitive to soft bands. Thus, some Swift-detected GRBs might be identified as XRFs by the biased detection, and even the intermediate class is elusive.…”

“…de Ugarte Postigo et al (2011) found that some GRBs in the Swift-detected sample are neither long nor short, and these GRBs can be classified as intermediate GRBs. The GRBs detected by BATSE and Fermi-GBM were further examined in the duration-hardness plane (Tarnopolski 2019). On the other hand, the GRB prompt spectrum can be described by the Band function.…”