Abstract
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 \(2.4\sigma\) 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.
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Bromberg, O., Nakar, E., Piran, T., Sari, R.: Astrophys. J. 764, 179 (2013)
Burnham, K.P., Anderson, D.R.: Sociol. Methods Res. 33, 261 (2004)
Chattopadhyay, T., Misra, R., Chattopadhyay, A.K., Naskar, M.: Astrophys. J. 667, 1017 (2007)
Desai, S.: Europhys. Lett. 115, 20006 (2016)
Desai, S., Liu, D.W.: Astropart. Phys. 82, 86 (2016)
Frontera, F., Guidorzi, C., Montanari, E., et al.: Astrophys. J. Suppl. Ser. 180, 192 (2009)
Gehrels, N., Ramirez-Ruiz, E., Fox, D.B.: Annu. Rev. Astron. Astrophys. 47, 567 (2009)
Horváth, I.: Astrophys. J. 508, 757 (1998)
Horváth, I.: Astron. Astrophys. 392, 791 (2002)
Horváth, I.: Astrophys. Space Sci. 323, 83 (2009)
Horváth, I., Tóth, B.G.: Astrophys. Space Sci. 361, 155 (2016)
Horváth, I., Balázs, L.G., Bagoly, Z., Veres, P.: Astron. Astrophys. 489, L1 (2008)
Horváth, I., Bagoly, Z., Balázs, L.G., et al.: Astrophys. J. 713, 552 (2010)
Huja, D., Mészáros, A., Řípa, J.: Astron. Astrophys. 504, 67 (2009)
Ivezić, Ž., Connolly, A., Vanderplas, J., Gray, A.: Statistics, Data Mining and Machine Learning in Astronomy. Princeton University Press, Princeton (2014)
Kass, R.E., Raftery, A.E.: J. Am. Stat. Assoc. 90, 773 (1995)
Koshut, T.M., Paciesas, W.S., Kouveliotou, C., et al.: Astrophys. J. 463, 570 (1996)
Kouveliotou, C., Meegan, C.A., Fishman, G.J., et al.: Astrophys. J. Lett. 413, L101 (1993)
Kouveliotou, C., Koshut, T., Briggs, M.S., et al.: In: Kouveliotou, C., Briggs, M.F., Fishman, G.J. (eds.) American Institute of Physics Conference Series. American Institute of Physics Conference Series, vol. 384, pp. 42–46 (1996)
Li, Y., Zhang, B., Lü, H.-J.: ArXiv e-prints (2016). arXiv:1608.03383
Liddle, A.R.: Mon. Not. R. Astron. Soc. 351, L49 (2004)
Liddle, A.R.: Mon. Not. R. Astron. Soc. 377, L74 (2007)
Liddle, A.R., Mukherjee, P., Parkinson, D.: ArXiv e-prints (2006). arXiv:astro-ph/0608184
Lien, A., Sakamoto, T., Barthelmy, S.D., et al.: Astrophys. J. 829, 7 (2016)
Lyons, L.: ArXiv e-prints (2016). arXiv:1607.03549
McBreen, B., Hurley, K.J., Long, R., Metcalfe, L.: Mon. Not. R. Astron. Soc. 271, 662 (1994)
Mukherjee, S., Feigelson, E.D., Jogesh Babu, G., et al.: Astrophys. J. 508, 314 (1998)
Nakar, E.: Phys. Rep. 442, 166 (2007)
Narayana Bhat, P., Meegan, C.A., von Kienlin, A., et al.: Astrophys. J. Suppl. Ser. 223, 28 (2016)
Paciesas, W.S., Meegan, C.A., Pendleton, G.N., et al.: Astrophys. J. Suppl. Ser. 122, 465 (1999)
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in FORTRAN. The Art of Scientific Computing (1992)
Shafer, D.L.: Phys. Rev. D 91, 103516 (2015)
Shi, K., Huang, Y.F., Lu, T.: Mon. Not. R. Astron. Soc. 426, 2452 (2012)
Tan, M.Y.J., Biswas, R.: Mon. Not. R. Astron. Soc. 419, 3292 (2012)
Tarnopolski, M.: Astron. Astrophys. 581, A29 (2015)
Tarnopolski, M.: Astrophys. Space Sci. 361, 125 (2016a)
Tarnopolski, M.: New Astron. 46, 54 (2016b)
Veres, P., Bagoly, Z., Horváth, I., Mészáros, A., Balázs, L.G.: Astrophys. J. 725, 1955 (2010)
Wilks, S.S.: Ann. Math. Stat. 9, 60 (1938)
Woosley, S.E., Bloom, J.S.: Annu. Rev. Astron. Astrophys. 44, 507 (2006)
Yang, E.B., Zhang, Z.B., Jiang, X.X.: Astrophys. Space Sci. 361, 257 (2016)
Zhang, B.: Nature 444, 1010 (2006)
Zhang, Z.-B., Choi, C.-S.: Astron. Astrophys. 484, 293 (2008)
Zhang, B., Zhang, B.-B., Virgili, F.J., et al.: Astrophys. J. 703, 1696 (2009)
Zhang, B., Lü, H.-J., Liang, E.-W.: ArXiv e-prints (2016a). arXiv:1611.01948
Zhang, Z.-B., Yang, E.-B., Choi, C.-S., Chang, H.-Y.: Mon. Not. R. Astron. Soc. 462, 3243 (2016b)
Zitouni, H., Guessoum, N., Azzam, W.J., Mochkovitch, R.: Astrophys. Space Sci. 357, 7 (2015)
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We would like to thank Peter Veres and the anonymous referee for valuable feedback and comments on the paper draft.
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Kulkarni, S., Desai, S. Classification of gamma-ray burst durations using robust model-comparison techniques. Astrophys Space Sci 362, 70 (2017). https://doi.org/10.1007/s10509-017-3047-6
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DOI: https://doi.org/10.1007/s10509-017-3047-6