Distributions of pseudo-redshifts and durations (observed and intrinsic) of Fermi GRBs

Abstract

Ever since the insightful analysis of the durations of gamma-ray bursts (GRBs) by Kouveliotou et al. (1993), GRBs have most often been classified into two populations: "short bursts" (shorter than 2.0 seconds) and "long bursts" (longer than 2.0 seconds). However, recent works have suggested the existence of an intermediate population in the bursts observed by the Swift satellite. Moreover, some researchers have questioned the universality of the 2.0-second dividing line between short and long bursts: some bursts may be short but actually result from collapsars, the physical mechanism behind normally long bursts, and some long ones may originate from mergers, the usual progenitors of short GRBs.In this work, we focus on GRBs detected by the Fermi satellite (which has a much higher detection rate than Swift and other burst-detecting satellites) and study the distribution of their durations measured in the observer's reference frame and, for those with known redshifts, in the bursts' reference frames. However, there are relatively few bursts with measured redshifts, and this makes an accurate study difficult. To overcome this problem, we follow Zhang and Wang (2018) and determine a "pseudo-redshift" from the correlation relation between the luminosity L p and the energy E p , both of which are calculated at the peak of the flux. Interestingly, we find that the uncertainties in the quantities observed and used in the determination of pseudo-redshifts, do affect the precision of the individual results significantly, but they keep the distribution of pseudo-redshifts very similar to that of the actual ones and thus allow us to use pseudo-redshifts for our statistical study. We briefly present the advantages and disadvantages of using pseudo-redshifts in this context.We use the reduced chi-square and the maximization of the log-likelihood to statistically analyze the distribution of Fermi GRB durations. Both methods show that the distribution of the observed (measured) and the intrinsic (source/rest frame) bursts durations are better represented by two groups/populations, rather than three.

Multivariate Analysis of BATSE Gamma-Ray Burst Properties Using Skewed Distributions

“…In case of Fermi Gamma-ray Burst Monitor (von Kienlin et al 2014;Gruber et al 2014;Narayana Bhat et al 2016), it was found that two Gaussian components suffice for the log T 90 distribution to be appropriately modeled (Bystřický et al 2012;Narayana Bhat et al 2016;Zhang et al 2016;Kulkarni & Desai 2017). The same conclusion was reached by Zitouni et al (2018) by employing pseudo-redshifts.…”

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

“…A third, intermediate class (Horváth 1998), remains putative. Its existence was claimed based on univariate and bivariate analyses of GRB observables modeled with Gaussian distributions (Mukherjee et al 1998;Horváth 2002;Horváth et al 2008;Zhang & Choi 2008;Huja et al 2009;Řípa et al 2009;Horváth et al 2010;Veres et al 2010;Zitouni et al 2015;Zhang et al 2016;Horváth et al 2018), but also has been put into doubt several times (Bystricky et al 2012;Řípa et al 2012;Tarnopolski 2015;Zitouni et al 2015;Narayana Bhat et al 2016;Tarnopolski 2016a,b;Ohmori et al 2016;Yang et al 2016;Kulkarni & Desai 2017;Zitouni et al 2018). Gaussian models, however, may not be the appropriate approach 1 (Koen & Bere 2012;Tarnopolski 2015;Koen & Bere 2017), as it has been already shown that the univariate distributions of T 90 (Tarnopolski 2016c,a;Kwong & Nadarajah 2018) and bivariate T 90 − H 32 ones (Tarnopolski 2019) are better described by mixtures of two skewed components rather than three Gaussian ones.…”

How does the shape of gamma-ray bursts' pulses affect the duration distribution?

“…One possible explanation could be that the redshift distribution, when convolved with the rest-frame duration distribution, leads to asymmetry. Assuming that the rest-frame logarithmic durations are Gaussian (as was the working paradigm, and was supported by modelling the redshift-equipped GRBs; Huja et al 2009;Tarnopolski 2016a;Zhang et al 2016;Zitouni et al 2018), it was however established that redshifts can account for only a few percent of the observed skewness (Tarnopolski 2020). It is worth pointing out that among the ∼ 1500 − 3000 GRBs observed in the BATSE, Swift, or Fermi samples each, only a total of a few hundred have an associated redshift estimate.…”