Classification of gamma-ray burst durations using robust model-comparison techniques

Kulkarni, S and Desai, Shantanu (2017) Classification of gamma-ray burst durations using robust model-comparison techniques. Astrophysics and Space Science. ISSN 0004-640X

Full text not available from this repository. (Request a copy)


Gamma-Ray Bursts (GRBs) have been conventionally bifurcated into two distinct categories dubbed “short” and “long”, depending on whether their durations are less than or greater than two seconds respectively. However, many authors have pointed to the existence of a third class of GRBs with mean durations intermediate between the short and long GRBs. Here, we apply multiple model comparison techniques to verify these claims. For each category, we obtain the best-fit parameters by maximizing a likelihood function based on a weighted superposition of two (or three) lognormal distributions. We then do model-comparison between each of these hypotheses by comparing the chi-square probabilities, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). We uniformly apply these techniques to GRBs from Swift (both observer and intrinsic frame), BATSE, BeppoSAX, and Fermi-GBM. We find that the Swift GRB distributions (in the observer frame) for the entire dataset favor three categories at about 2.4σ from difference in chi-squares, and show decisive evidence in favor of three components using both AIC and BIC. However, when the same analysis is done for the subset of Swift GRBs with measured redshifts, two components are favored with marginal significance. For all the other datasets, evidence for three components is either very marginal or disfavored.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Desai, Shantanu
Item Type: Article
Subjects: Physics
Divisions: Department of Physics
Depositing User: Team Library
Date Deposited: 30 May 2019 09:06
Last Modified: 30 May 2019 10:17
Publisher URL:
Related URLs:

    Actions (login required)

    View Item View Item
    Statistics for RAIITH ePrint 5388 Statistics for this ePrint Item