Two Dimensional Clustering of Gamma-Ray Bursts using durations and hardness

Abstract

Gamma-Ray Bursts (GRBs) have been conventionally bifurcated into two distinct categories: "short" and "long" with durations less than and greater than two seconds respectively. However, there is a lot of literature (although with conflicting results) regarding the existence of a third intermediate class. To investigate this issue, we extend a recent study by Kulkarni & Desai 2017a on classification of GRBs to two dimensions by incorporating the GRB hardness in addition to the observed durations. We carry out this unified analysis on GRB datasets from four detectors, viz. BATSE, RHESSI, Swift (observed and intrinsic frame), and Fermi-GBM. We consider the duration and hardness features in log-scale for each of these datasets and determine the best-fit parameters using Gaussian Mixture Model. This is followed by information theoretic criterion (AIC and BIC) to determine if a threecomponent fit is favored compared to a two-component one or vice-versa. For BATSE, we find that both AIC and BIC show preference for three components with decisive significance. For Fermi and RHESSI, both AIC and BIC show preference for two components, although the significance is marginal from AIC, but decisive using BIC. For Swift dataset in both the observed and rest frame, we find that three components are favored according to AIC with decisive significance, and two Aishwarya Bhave

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.…”

“…2008;Guoshen Yu et al 2012). Gaussian distributions have been adopted by many GRB classification studies (Chattopadhyay & Maitra 2017;Svinkin et al 2019;Bhave et al 2017;Tarnopolski 2019a,b). In this work, we assume the distributions of each subclass of GRBs to be Gaussian in logarithmic space and use GMM to derive the properties of the overall population from those subclasses.…”

“…With new methods adopted, however, some new and different results or conclusions about GRB classification have been proposed. Bhave et al (2017) analyzed two-dimensional clustering of GRBs using duration and hardness and obtained different classification conclusions of either two or three components for different detectors or datasets. Svinkin et al (2019) analyzed GRBs detected by Konus-Wind with similar methods and also suggested a three-component classification.…”

Two Dimensional Clustering of Swift/BAT and Fermi/GBM Gamma-ray Bursts

“…The clustering of the duration and hardness of Swift/BAT GRBs [53,54] and the clustering of light curve shape indicators [55] identified three classes of bursts. Gaussian Mixture Modelbased (GMM) clustering applied to the Fermi/GBM sample revealed that GRB 170817A fit within the intermediate class in the duration-hardness plane [56], and that five classes could be identified by clustering spectral fit parameters, fluences, and durations [57].…”

A comprehensive statistical study on gamma-ray bursts

“…Chattopadhyay & Maitra (2017) even found five kinds of GRBs in the BATSE catalog using Gaussian Mixture model. However, some recent works still insisted on two components (Tarnopolski 2015(Tarnopolski , 2016Zhang et al 2016a;Yang et al 2016b;Kulkarni & Desai 2017;Bhave et al 2017), and showed the intermediate GRB class is unlikely. The plausible explanation of the duration bimodal distribution is that the two-class GRB duration distributions are intrinsically non-symmetrical (Tarnopolski 2016).…”