Detailed Classification of Swifts Gamma-Ray Bursts

arXiv:1003.0632v1 [astro-ph.HE] 2 Mar 2010 Detailed Classification of Swift’s Gamma-Ray Bursts I. Horváth Department of Physics, Bolyai Military University, H-1581 Budapest, POB 15, Hungary horvath.istvan@zmne.hu Z. Bagoly Dept. of Physics of Complex Systems, Eötvös University, H-1117 Budapest, Pázmány P. s. 1/A, Hungary L. G. Balázs Konkoly Observatory, H-1505 Budapest, POB 67, Hungary A. de Ugarte Postigo European Southern Observatory, Casilla 19001, Santiago 19, Chile Osservatorio Astronomico di Brera (INAF-OAB), via E. Bianchi 46, I-23807, Merate (LC), Italy P. Veres Dept. of Physics of Complex Systems, Eötvös University, H-1117 Budapest, Pázmány P. s. 1/A, Hungary Department of Physics, Bolyai Military University, H-1581 Budapest, POB 15, Hungary and A. Mészáros Faculty of Mathematics and Physics, Charles University, Astronomical Institute, V Holešovičkách 2, 180 00 Prague 8, Czech Republic ABSTRACT – 2 – Earlier classification analyses found three types of gamma-ray bursts (short, long and intermediate in duration) in the BATSE sample. Recent works have shown that these three groups are also present in the RHESSI and the BeppoSaX databases. The duration distribution analysis of the bursts observed by the Swift satellite also favors the three-component model. In this paper, we extend the analysis of the Swift data with spectral information. We show, using the spectral hardness and the duration simultaneously, that the maximum likelihood method favors the three-component against the two-component model. The likelihood also shows that a fourth component is not needed. Subject headings: gamma-rays: bursts, methods: statistical, data analysis 1. Introduction Decades ago Mazets et al. (1981) and Norris et al. (1984) suggested that there might be a separation in the duration distribution of gamma-ray bursts (GRBs). Kouveliotou et al. (1993) found bimodality in the distribution of the logarithms of the durations. Today it is widely accepted that the physics of these two groups (short and long bursts — called also as Type I and Type II classes (Zhang et al. 2007; Kann et al. 2008; Zhang et al. 2009; Lü et al. 2010)) — are different, and these two kinds of GRBs are different phenomena (Norris et al. 2001; Balázs et al. 2003; Fox et al. 2005; Kann et al. 2008). The angular sky distribution of the short BATSE’s GRBs is anisotropic (Vavrek et al. 2008). In the Swift database (Sakamoto et al. 2008), the measured redshift distributions for the two groups are also different: for short bursts the median is 0.4 (O’Shaughnessy et al. 2008) and for the long ones it is 2.4 (Bagoly et al. 2006). In the Third BATSE Catalog (Meegan et al. 1996) — using uni- and multi-variate anal- yses — Horváth (1998) and Mukherjee et al. (1998) found a third type of GRBs. Later several papers (Hakkila et al. 2000; Balastegui et al. 2001; Rajaniemi & Mähönen 2002; Horváth 2002; Hakkila et al. 2003; Horváth et al. 2004; Borgonovo 2004; Horváth et al. 2006; Chattopadhyay et al. 2007) confirmed the existence of this third ("intermediate" in duration) group in the same database. The celestial distribution of this third group in the BATSE sample is also anisotropic (Mészáros et al. 2000a,b; Litvin et al. 2001; Magliocchetti et al. 2003; Vavrek et al. 2008). Recent works analyzed the Swift (Horváth et al. 2008; Huja et al. 2009), RHESSI (Řípa et al. 2008, 2009) and BeppoSaX (Horváth 2009) data, respectively. They have found the intermediate class in all the three satellites’ data: in the Swift database the one- – 3 – dimensional maximum likelihood (ML) analysis of the durations has proven the existence of these three subgroups (Horváth et al. 2008); a preliminary study by the same method of the BeppoSAX database (Frontera et al. 2009) gave support for this class (Horváth 2009); in the RHESSI database two methods led to the same results - the same one-dimensional ML method of the durations and the bivariate ML method using both duration and hardness (Řípa et al. 2008, 2009). A method to infer the physical origin of GRBs was developed recently (Zhang et al. 2007; Kann et al. 2008; Zhang et al. 2009). Many other observed parameters besides du- ration are used as the differentiation criteria. Such a scheme only result in two major types of GRBs (Type I and Type II). On the other hand, GRBs may be classified using different parameters other than duration (e.g., Lü et al. (2010)). We shall discuss these more in the discussion section. Horváth et al. (2006) analyzed the BATSE data using duration and hardness simul- taneously; Řípa et al. (2009) studied the RHESSI data with the same configuration. The bivariate analysis on the Swift database has not been done yet. Horváth et al. (2008) only provided the one-dimensional analysis of the durations. Hence, to get a complete picture, one has to analyze the Swift data also - using both the duration and hardness simultaneously - with the bivariate ML method. This is the aim of this paper. The paper is organized as foll

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