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Issue
A&A
Volume 609, January 2018
Article Number A112
Number of page(s) 19
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/201731598
Published online 25 January 2018

© ESO, 2018

1. Introduction

The relativistic nature of gamma-ray bursts (GRBs) was originally posed on theoretical grounds (Goodman 1986; Paczynski 1986; Krolik & Pier 1991; Fenimore et al. 1993; Baring & Harding 1997; Lithwick & Sari 2001): the small size of the emitting region, as implied by the observed millisecond variability, would make the source opaque due to γ–γ pair production unless it expands with bulk Lorentz factor Γ0 ~ 100–1000 (e.g. Piran 1999). Refinements of this argument applied to specific GRBs (Abdo et al. 2009a,b; Ackermann et al. 2010a; Hascoët et al. 2012; Zhao et al. 2011; Zou & Piran 2010; Zou et al. 2011; Tang et al. 2015) led to some estimates of Γ0. A direct confirmation that GRB outflows are relativistic was found in 970508 (Frail et al. 1997): the suppression of the observed radio variability ascribed to scintillation induced by Galactic dust provided an estimate of the source relativistic expansion. Similarly, the long-term monitoring in the radio band of GRB 030329 (Pihlström et al. 2007; Taylor et al. 2004, 2005) allowed to set limits on the expansion rate (Mesler et al. 2012).

In the standard fireball scenario, after an optically thick acceleration phase the ejecta coast with constant bulk Lorentz factor Γ0 before decelerating due to the interaction with the external medium. Γ0 represents the maximum value attained by the outflow during this dynamical evolution (see Kumar & Zhang for a recent review). Direct estimates of Γ0 became possible in the last decade thanks to the early follow up of the afterglow emission. The detection of an early afterglow peak, tp ~ 150–200 s, in the near-infrared light curve of GRB 060418 and GRB 060607A provided one of the first estimates of Γ0 (Molinari et al. 2007).

The recent development of networks of robotic telescopes (ROTSE–III: Akerlof et al. 2003; GROCSE: Park et al. 1997; TAROT: Klotz et al. 2009; SkyNet: Graff et al. 2014; WIDGET: Urata et al. 2011; MASTER: Lipunov et al. 2004; Pi of the Sky: Burd et al. 2005; RAPTOR: Vestrand et al. 2002; REM: Zerbi et al. 2001; Watcher: Ferrero et al. 2010) has allowed us to follow up the early optical emission of GRBs. Systematic studies (Liang et al. 2010; Lü et al. 2012; Ghirlanda et al. 2012, G12 hereafter) derived the distribution of Γ0 and its possible correlation with other observables. G12 found:

that different distributions of Γ0 are obtained according to the density profile of the circumburst medium;

the existence of a correlation (tighter with respect to that with Eiso);

the presence of a linear correlation Γ0 ∝ Ep.

As proposed by G12, the combination of these correlations provides a possible interpretation of the spectral energy correlations Ep−Eiso (Amati et al. 2002) and Ep−Liso (Yonetoku et al. 2004) as the result of larger Γ0 in bursts with larger luminosity/energy and peak energy. Possible interpretations of the correlations between Γ0 and the GRB luminosity have been proposed in the context of neutrino or magneto–rotation powered jets (Lü et al. 2012; Lei et al. 2013). G12 (see also Ghirlanda et al. 2013a) showed that a possible relation between Γ0 and the jet opening angle θjet could also justify the Ep−Eγ correlation (here Eγ is the collimation corrected energy).

In order to estimate Γ0, we need to measure the onset time tp of the afterglow. If the circumburst medium is homogeneous, this is revealed by an early peak in the light curve, corresponding to the passage from the coasting to the deceleration phase of the fireball. On the other hand, if a density gradient due to the progenitor wind is present, the bolometric light curve is constant until the onset time. However, also in the wind case, a peak could be observed if pair production ahead of the fireball is a relevant effect as discussed in G12, for example.

G12 considered 28 GRBs with a clear peak in their optical light curve, and included three GRBs with a peak in their GeV light curves (as observed by the Large Area Telescope – LAT – on board Fermi). Early tp measurements are limited by the time needed to start the follow–up observations. The LAT (0.1–100 GeV), with its large field of view, performs observations simultaneously to the GRB prompt emission for GRBs happening within its field of view. The detection of an early peak in the GeV light curve, if interpreted as afterglow from the forward shock (e.g. Ghisellini et al. 2010; Kumar & Barniol Duran 2010), provides the estimate of the earliest tp (i.e. corresponding to the largest Γ0). In the short GRB 090510, the LAT light curve peaks at ~0.2 s corresponding to Γ0 ~ 2000 (Ghirlanda et al. 2010; Ackermann et al. 2010b). Recently it has been shown that upper limits on Γ0 can be derived from the non-detection of GRBs by the LAT (Nava et al. 2017) and that such limits are consistent with lower limits and detections reported in the literature.

Precise and fast localisations of GRB counterparts, routinely performed by Swift, coupled to efficient follow up by robotic telescope networks, allowed us to follow the optical emission starting relatively soon after the GRB trigger. However, a delay of a few hundred seconds can also induce a bias against the measure of early–intermediate tp values (Hascoët et al. 2012). As argued by Hascoët et al. (2012), the distribution of Γ0, derived through measured tp, could lack intermediate to large values of Γ0 (corresponding to intermediate to early values of tp) and the Γ0–Eiso correlation could be a boundary, missing several bursts with large Γ0 (i.e. because of the lack of early tp measurements).

For this reason, upper limits are essential to derive the distribution of Γ0 in GRBs and its possible correlation with other prompt emission properties (Eiso, Liso, and Ep) and, in general, to study the comoving frame properties of the population. To this aim, in this paper (i) we collect the available bursts with an optical tp, expanding and revising the previously published samples; and (ii) we collect a sample of bursts with upper limits on tp. Through this censored data sample we reconstruct the distribution of Γ0 accounting (for the first time) for upper limits. We then employ Monte Carlo methods to study the correlations between Γ0 and the rest frame isotropic energy/luminosity and peak energy.

The sample selection and its properties are presented in Sects. 2–4, respectively. The different formulae for the estimate of the bulk Lorentz factor Γ0 appearing in the literature are presented and compared in Sect. 5. In Sect. 6 the distribution of Γ0 and its correlation (Sect. 7) with Eiso, Liso, and Ep are studied. Discussion and conclusions follow in Sect. 8. We assume a flat cosmology with h = ΩΛ = 0.7.

2. The sample

We consider GRBs with measured redshift z and well constrained spectral parameters of the prompt emission. For these events it is possible to estimate the isotropic energy Eiso and luminosity Liso and the rest frame peak spectral energy Ep (i.e. the peak of the νFν spectrum).

Γ0 can be estimated from the measure of the peak tp of the afterglow light curve interpreted as due to the deceleration of the fireball. We found in the literature 67 GRBs (66 long and 1 short) with an estimate of tp (see Table A.1). Of these, 59 tp are obtained from the optical and 8 from the GeV light curves. Through a systematic search of the literature we collected 106 long GRBs whose optical light curve, within one day of the trigger, decays with no apparent tp. These GRBs provide upper limits . Details of the sample selection are reported in the following sections.

2.1. Afterglow onset tp

G12 studied a sample of 30 long GRBs, with tp measured from the optical (27 events) or from the GeV (3 events) light curves. We revise the sample of G12 with new data recently appearing in the literature, and we extend it, beyond GRB 110213A, including all new GRBs up to July 2016 with an optical or GeV afterglow light curve showing a peak tp.

Bursts with a peak in their early X-ray emission are not included in our final sample because the X-ray can be dominated (a) by an emission component of “internal” origin, for example, due to the long lasting central engine activity (e.g. Ghisellini et al. 2007; Genet et al. 2007; Ioka et al. 2006; Panaitescu 2008; Toma et al. 2006; Nardini et al. 2010) and/or (b) by bright flares (Margutti et al. 2010)1.

We excluded from our final sample (i) bursts with a multi–peaked optical light curve at early times2; and (ii) events with an optical peak preceded by a decaying light curve (e.g. GRB 100621A, GRB 080319C present in G12) since the early decay suggests the possible presence of a multi–peaked structure. The latter events, however, were included in the sample of (Sect. 2.2), considering the earliest epoch of their optical decay.

2.1.1. The gold sample

Table A.1 lists all the GRBs we collected. The “Gold” sample is composed of sources with a complete set of information, namely measured tp (Col. 6) and spectral parameters (Cols. 3–5). It contains 49 events: 48 long GRBs plus the short event 090510. GRBs of the Gold sample have the label “(g)” at the end of their names reported in Col. 1 of Table A.1. The redshift z, rest frame peak energy Ep, isotropic energy and luminosity (Eiso and Liso, respectively) are given in Table A.1. Eight out of 49 GRBs have their tp measured from the GeV light curve as observed by the Fermi–LAT (labelled “L” or “SL” for the short GRB 090510).

For GRB 990123, GRB 080319B, and GRB 090102 reported in Table A.1, it has been proposed that the early optical emission (and the observed peak) is produced by either the reverse shock (RS; Bloom et al. 2009; Japelj et al. 2014; Sari & Piran 1999) or by a combination of forward and reverse shock (Gendre et al. 2010; Steele et al. 2009) or even by a two-component jet structure (e.g. Racusin et al. 2008,for GRB 080319B). We assume for these three GRBs that the peak is due to the outflow deceleration and include them in our sample.

thumbnail Fig. 1

Cumulative distribution of the afterglow onset time tp (red solid line) in the observer frame for the 66 long GRBs of the “Gold+Silver” sample. The black dashed line (marked with leftward arrows) is the cumulative distribution of 85 upper limits on tp (filtered from Table A.1 according to s). The black solid line (shaded yellow region) is the reconstructed tp distribution (95% confidence interval) accounting for through the Kaplan Meier estimator. The dotted and dot–dashed lines are the cumulative distributions of T90 and Tp,γ (i.e. the time of the peak of the prompt emission light curve), respectively, for the 151 long GRBs. The distributions have been normalised to the respective sample size. Insert: Distribution of tp (solid blue line) and of upper limits (dashed cyan line with leftward arrows) of the 50 GRBs of our sample (16 with tp and 34 with ) also present in the Swift BAT complete sample (Salvaterra et al. 2012).

2.1.2. The silver sample

In our search we found 18 events with tp but with poorly constrained prompt emission properties (Ep, Eiso, and Liso). In most of these cases, the Swift Burst Alert Telescope (BAT) limited (15–150 keV) energy band coupled with a relatively low flux of the source prevent us from constraining the peak of the spectrum (Ep) even when it lies within the BAT energy range. Most of these BAT spectra were fitted by a simple power law model. Sakamoto et al. (2011) showed that, also in these cases, the Ep could be derived adopting an empirical correlation between the spectral index of the power law, fitted to the BAT spectrum, and Ep. This empirical correlation was derived and calibrated with those bursts where BAT can measure Ep. Alternatively, Butler et al. (2007, 2010) proposed a Bayesian method to recover the value of Ep for BAT spectra fitted by the simple power law model.

We adopted the values of Ep and Eiso calculated by Butler et al. (2007, 2010) for 15/18 GRBs in common with their list and the Sakamoto et al. (2011) relation for the remaining 3/18 events in order to exploit the measure of tp also for these 18 bursts. Sakamoto et al. (2011) and Butler et al. (2007, 2010) study the time-integrated spectrum of GRBs. We estimated the luminosity Liso = Eiso(P/F), where P and F are the peak flux and fluence, respectively, in the 15–150 keV energy range. GRBs of the “Silver” sample are labelled “(s)” in Table A.1.

While we made this distinction explicit for clarity, in what follows we use the total sample of tp without any further distinction between the Gold and Silver samples.

2.2. Upper limits on tp

The afterglow onset is expected within one day for typical GRB parameters (see Sect. 4). Several different observational factors, however, can prevent the measure of tp. It is hard to construct a sample of upper limits . Hascoët et al. 2014 included some tp in their analysis but without a systematic selection criterion.

In this paper we collect from the literature all the GRBs with known z and with an optical counterpart observed at least three times within one day of the trigger. If the light curve is decaying in time we set the upper limit corresponding to the earliest optical observation. Similar criteria apply if the long-lived afterglow emission is detected in the GeV energy range by the LAT. For several recent bursts, highly sampled early light curves are available. We excluded events with complex optical emission at early times and selected only those with an indication of a decaying optical flux.

The 106 GRBs with are reported in Table A.1. For the purposes of our analysis in the following we use a subsample of the 85 most constraining , that is, those with s which corresponds to five times the largest value of tp of the Gold+Silver sample.

3. Sample properties

In this section we present the distribution of tp (in the observer and rest frame) and study the possible correlation of the rest frame tp with the observables of the prompt emission.

3.1. Distribution of the observer frame tp

Figure 1 shows the cumulative distribution (red line) of the observer frame afterglow peak time tp of long GRBs3. The distribution of upper limits is shown by the dashed black line (with leftward arrows). The distribution of measured tp is consistent with that of the upper limits at the extremes, that is, below 30 s and above ~1000 s. In particular, the low–end of the distribution of tp is mainly composed of bursts whose onset time is provided by the LAT data. Considering only GRBs with measured tp (red line in Fig. 1), the (log) average tp ~ 230 s while upper limits (dashed black line in Fig. 1) have a (log) average tp ~ 160 s. The relative position of the two distributions (red and black dashed) suggests that if we considered only tp measurements (as in G12; Liang et al. 2010; Lü et al. 2012) we would miss several intermediate–early onsets. This is confirmed also if we consider only the GRBs present in our sample which are part of the so called “BAT6” sample (Salvaterra et al. 2012). Indeed, this high flux cut sample of 58 Swift GRBs is 90% complete in redshift. There are 16 GRBs in our sample with measured tp and 34 with in common with the BAT6 sample (i.e. 86% of the sample). Their tp distribution (and the distribution of their ) is shown in the insert of Fig. 1. Similarly to the larger sample, the distribution of tp for the BAT6 sample is close to that of upper limits .

In our sample nearly half of the bursts have tp measured and half are upper limits. The distributions of tp and overlap considerably ensuring that random censoring is present. Survival analysis (Feigelson & Nelson 1985) can be used to reconstruct the true distribution of tp. We use the non–parametric Kaplan–Meier estimator (KM), as adapted by Feigelson & Nelson (1985) to deal with upper limits. The KM reconstructed CDF is shown by the solid black line in Fig. 1. The 95% confidence interval on this distribution (Miller 1981; Kalbfleish & Prentice 1980) is shown by the yellow shaded region in Fig. 1. The median value of the CDF is ⟨ tp ⟩ = 60 ± 20 s (1σ uncertainty). We verified that, considering a more stringent subsample of upper limits, that is, (tp), similar CDF and average values are obtained.

3.2. Distribution of the rest frame tp

Figure 2 shows the cumulative distribution of tp in the rest frame. Colour and symbols are the same as in Fig. 1. In particular we note that also in the rest frame the cumulative distribution of measured tp (solid red line) is close to the distribution of upper limits (leftward arrows). The KM estimator leads to a reconstructed rest frame tp distribution (solid black-yellow shaded curve) which is distributed between 1 and 103 s with an average value of s. The insert of Fig. 2 shows the distribution of the onset time of the GRBs belonging to the complete Swift sample. Again the measured tp distribution (solid blue line) is close to the distribution of upper limits, suggesting the presence of a selection bias against the measurement of the earliest tp values which is, however, not due to the requirement of the measure of the redshift.

thumbnail Fig. 2

Cumulative distribution of the afterglow onset time tp in the rest frame. Same symbols and colour code as in Fig. 1.

Table 1

Correlation between rest frame tp/ (1 + z) and prompt emission properties (Fig. 4).

3.3. Comparison between tp, T90, and Tp,γ

One assumption for the estimate of Γ0 from the measure of tp (see Sect. 4) is that most of the kinetic energy of the ejecta has been transferred to the blast wave (so called “thin shell approximation” – Hascoët et al. 2014) which is decelerated by the circumburst medium. Therefore, we should expect that tp be larger than the duration of the prompt emission, estimated by T90. To check this hypothesis, we collected T90 for the bursts of our sample: its distribution is shown by the grey dotted line in Fig. 1. A scatter plot showing T90 vs. the observer frame tp is shown in the top panel of Fig. 3: bursts with measured tp are shown by the red filled circles (GRBs with tp derived from the GeV–LAT light curve are shown by the star symbols), upper limits are also shown by the black (green for LAT bursts) symbols. The majority (80%) of GRBs lie below the equality line (dashed line in Fig. 3) having tp>T90. 20% of the bursts have tp<T90. A generalised Spearman’s rank correlation test (accounting also for upper limits – Isobe et al. (1986, 1990)) indicates no significant correlation between T90 and tp (at >3σ level of confidence).

The prompt emission of GRBs can be highly structured with multiple peaks separated by quiescent times. While T90 is representative of the overall duration of the burst, another interesting timescale is the peak time of the prompt emission light curve Tp,γ. This time corresponds to the emission of a considerable fraction of energy during the prompt and it is worth comparing it with tp. The distribution of Tp,γ is shown by the dot–dashed line in Fig. 1. The bottom panel of Fig. 3 compares Tp,γ with tp. Noteworthily, no GRB has tp<Tp,γ. There are only two upper limits from the early follow up of the GeV light curve (green arrows in the bottom panel of Fig. 3) which lie above the equality line. However, the large uncertainties in these two bursts (090328 and 091003) on their GeV light curve at early times (Panaitescu 2017) make them also compatible with having tp>Tp,γ. Again, the Spearman’s generalised test results in no significant correlation between tp and Tp,γ.

thumbnail Fig. 3

Top panel: GRB duration T90 vs. afterglow peak time tp. GRBs with measured tp are shown with red circles (green stars for LAT bursts). Upper limits on tp are shown by the black arrows (green arrows for LAT GRBs). Bottom panel: time of the peak of the prompt emission light curve Tp,γ vs. tp. Same symbols and colours as in the top panel. In both panels the equality is shown by the dashed line and the short GRB 090510 is shown by the green square.

3.4. Empirical correlations

We study the correlation between the onset time tp in the rest frame and the energetic of GRBs. Figure 4 shows tp/(1 + z) vs. the prompt emission isotropic energy Eiso, isotropic luminosity Liso and rest frame peak energy Ep.

GRBs with measured tp (red circles and green stars in Fig. 4) show significant correlations (chance probabilities <10-5 – Table 1) shown by the red solid lines (obtained by a least square fit with the bisector method) in the panels of Fig. 4. The correlation parameters (slope and normalisation) obtained only with tp are reported in the left part of Table 1.

Upper limits (black downward arrows in Fig. 4) are distributed in the same region of the planes occupied by tp. Figure 4 shows also the lower limits on (grey upward arrows) derived assuming that tp>Tp,γ.

The 85 GRBs without a measured tp should have their onset corresponding to the vertical interval limited by the grey and black arrows in Fig. 4. Indeed, the reconstructed distribution of tp (shown by the solid black line in Fig. 1) is bracketed by the cumulative distribution of Tp,γ on the left–hand side (dot–dashed grey line in Fig. 1) and by the distribution of on the right–hand side (dashed black line in Fig. 1).

In order to evaluate the correlations of Fig. 4 combining measured tp and upper/lower limits we adopted a Monte Carlo approach. We assume that the KM estimator provides the distribution of tp of the population of GRBs shown by the solid black line in Fig. 1. For each of the 85 GRBs with upper limits we extract randomly from the reconstructed tp distribution a value of tp (requiring that the extracted value tp(i) falls within the range – where i runs from 1 to 85) and combine them with the 66 GRBs with measured tp to compute the correlation (using the bisector method). We repeat this random extraction obtaining 105 random samples and compute the average values of the Spearman’s rank correlation coefficient, of its chance probability, and of the slope and normalisation of the correlations (fitted to the 105 randomly generated samples). In Fig. 4, the average correlation obtained through this Monte Carlo method is shown by the dot-dashed black line. The average values of the rank correlation coefficient, its probability, and the correlation parameters (slope and normalisation) are reported in the right section of Table 1.

These results show that significant correlations exist between the observables (i.e. fully empirical at this stage). A larger energy/luminosity/peak energy corresponds to an earlier tp. The distribution of upper () and lower (Tp,γ) limits in the planes of Fig. 4 show that these planes cannot be uniformly filled with points, further supporting the existence of these correlations.

thumbnail Fig. 4

Left panel: tp/ (1 + z) vs. Eiso. GRBs with estimates of tp are shown with filled red circles (green symbols for LAT events). Upper limits on tp (Table A.1) are shown by the black arrows (green arrows for LAT events). Middle panel: tp/ (1 + z) vs. Liso. Right panel: tp/ (1 + z) vs. Ep. In all panels the short GRB 090510 is shown by the green square symbol.

4. On the origin of the afterglow peak time tp

In the following we assume that the afterglow peak tp is produced by the fireball deceleration. However, other effects can produce an early peak in the afterglow light curve: tp can be due to the passage across the observation band of the characteristic frequencies of the synchrotron spectrum. In this case, however, any of the characteristic synchrotron frequencies should lie very close to the observation band at the time of the peak.

The synchrotron injection frequency is (Panaitescu & Kumar 2000): (1)where ϵe and ϵB represent the fraction of energy shared between electrons and magnetic field at the shock and η is the efficiency of conversion of kinetic energy to radiation (i.e. Eiso,53/η represents the kinetic energy in units of 1053 ergs in the blast wave). Here t is measured in days in the source rest frame. The above expression for νinj(t) is valid either if the circumburst medium has constant density or if its density decreases with the distance from the source as r-2 (wind medium); in the latter case only the normalisation constant is larger by a factor ~2.

thumbnail Fig. 5

Injection frequency at the tp for each GRB. The optical-R frequency is show by the horizontal line. The injection frequency is shown for the homogeneous and wind case with red and blue symbols. GRBs with tp from the LAT light curve are shown with star symbols. The scaling t− 3/2 of the injection frequency is shown for reference (it is not a fit) by the dashed line. Vertical bars, shown only for the red symbols, represent the position of the injection frequency obtained assuming ϵB in the range 10-4−10-1.

Figure 5 shows νinj(t = tp) (red and blue symbols for the homogeneous or wind medium case and stars for the LAT bursts) with respect to the optical R frequency (solid horizontal line). We assumed typical values of the shock parameters: ϵB = 0.01 and ϵe = 0.1 and an efficiency η = 20% (Nava et al. 2014; Beniamini et al. 2015). The value ϵe = 0.1 is consistent with Beniamini & van der Horst (2017) and Nava et al. (2014) who recently found a narrow distribution of this parameter as inferred from the analysis of the radio and GeV afterglow, respectively. ϵB is less constrained and has a wider dispersion, between 10-4 and 10-1 (Granot & van der Horst 2014; Santana et al. 2014; Zhang et al. 2015; Beniamini et al. 2016), which translates into a factor of 10 for the value of νinj (vertical lines in Fig. 5) obtained assuming ϵB = 0.01 (open circles in Fig. 5) . In order to account for this uncertainty we show, as vertical lines in Fig. 5 (for the open circles only for clarity), the possible range of frequencies that are obtained assuming ϵB ∈ [10-4,10-1]. In all bursts, the injection frequency, when the afterglow peaks (i.e. at tp), is above the optical band and it cannot produce the peak as we see it4.

However, one may argue that the above argument depends on the assumed typical values of ϵe and ϵB. Consider, for example, the homogeneous case s = 0. In order to “force” νinj = νopt,R = 4.86 × 1014 Hz at t = tp and reproduce the observed flux at the peak Fp, one would require a density of the ISM: (2)where D28 is the luminosity distance in units of 1028 cm and tp,sec,−2 is the rest frame peak time, now expressed in units of 100 s. For typical peak flux of 10 mJy we should have densities ~ 105 cm-3.

The other synchrotron characteristic frequency which could evolve and produce a peak when passing across the optical band is the cooling frequency νcool. For the homogeneous medium Hz, where n is the number density of the circum burst medium and Y the Compton parameter (Panaitescu & Kumar 2000). In this case, the portion of the synchrotron spectrum which could produce a peak is ν < νcool, under the assumption that νcool <νinj, and the flux evolution should be F(t) ∝ t1/6 (Panaitescu 2017; Sari et al. 1998). This is much shallower than the rising slopes of the afterglow emission of most of the bursts before tp (Liang et al. 2013). In the wind case, the cooling break increases with time ∝t1/2 and, if transitioning accross the observing band from below, it should produce a decaying afterglow flux F(t) ∝ t− 1/4 and not a peak.

Therefore, we are confident that tp cannot be produced by any of the synchrotron frequencies crossing the optical band and it can be interpreted as due to the deceleration of the fireball and used to estimate Γ0.

5. Estimate of Γ0

In this section we revise, in chronological order, the different methods and formulae proposed for estimating the bulk Lorentz factor Γ0 (Ghirlanda et al. 2012; Ghisellini et al. 2010; Molinari et al. 2007; Nava et al. 2017; Sari & Piran 1999). The scope is to compare these methods and quantify their differences. We consider only the case of an adiabatic evolution of the fireball propagating in an external medium with a power law density profile n(R) = n0R− s. The general case of a fully radiative (and intermediate) emission regime is discussed in Nava et al. (2013). In Sect. 5 we present the results, that is, estimates of Γ0, for the two popular cases s = 0 (homogeneous medium) and s = 2 (wind medium). The estimate and comparison of Γ0 in these two scenarios for a sample of 30 GRBs was presented, for the first time, in G12.

During the coasting phase the bulk Lorentz factor is constant (Γ = Γ0) and the bolometric light curve of the afterglow scales as Liso ∝ t2−s. After the deceleration time, Γ0 starts to decrease and, in the adiabatic case, the light curve scales as Liso ∝ t-1 independently from the value of s.

Therefore, for a homogeneous medium (s = 0) the light curve has a peak, while in the wind case (s = 2) the light curve should be flat before tp  and steeper afterwards5. Γ0 is the bulk Lorentz factor corresponding to the coasting phase. It is expected that the outflow is discontinuous with a distribution of bulk Lorentz factors (e.g. to develop internal shocks). Γ0 represents the average bulk Lorentz factor of the outflow during the coasting phase.

5.1. Sari & Piran (1999)

In (Sari & Piran 1999, hereafter SP99) Γ0 is derived assuming that tp corresponds to the fireball reaching the deceleration radius Rdec. This is defined as the distance from the central engine, where the mass of the interstellar medium m(Rdec), swept up by the fireball, equals M0/ Γ0: (3)where E0 is the isotropic equivalent kinetic energy of the fireball after the prompt phase.

SP99 assume as the link between the deceleration time tdec and Rdec. This assumption corresponds implicitly to considering that the fireball travels up to Rdec with a constant bulk Lorentz factor equal to Γ0, or, in other words, that the deceleration starts instantaneously at this radius. Instead, the deceleration of the fireball starts before Rdec. This approximation underestimates the deceleration time tdec and, consequently, underestimates Γ0: (4)where tp,z ≡ tp/ (1 + z). The original formula reported in SP99 is valid only for a homogeneous medium. Here, Eq. (2) has been generalised for a generic power law density profile medium.

5.2. Molinari et al. (2007)

Molinari et al. (2007, hereafter M07) introduce a new formula for Γ0, obtained from SP99 by modifying the assumption on Rdec. They, realistically, consider that the deceleration begins before Rdec so that Γdec< Γ0. Under this assumption, and . However, they assume Γdec = Γ0/ 2 obtaining: (5)This estimate of Γ0 is a factor 2 larger than that obtained by SP99: as discussed in Nava et al. (2013), M07 overestimate6 the deceleration radius by a factor ~ 2 and, consequently, the value of Γ0 is overestimated by the same factor. This is consistent with the results of numerical one-dimensional (1D) simulations of the blast wave deceleration (Fukushima et al. 2017). Their results suggest that Γ0 should be a factor ~2.8 smaller than that derived (e.g. by Liang et al. 2010) through Eq. (5).

5.3. Ghisellini et al. (2010)

A new method to calculate the afterglow peak time tp is presented in (Ghisellini et al. 2010, hereafter G10). This method does not rely on the definition of the deceleration radius as in SP99 and M07. G10 derive tp by equating the two different analytic expressions for the bolometric luminosity as a function of the time L(t) during the coasting phase and during the deceleration phase7.

The relation linking the radius and the time is assumed to be: R = 2actΓ2, where a = 1 during the coasting phase and a> 1 during the deceleration one. In the latter case, its value depends on the relation between the bulk Lorentz factor Γ and the radius R: for an adiabatic fireball, for instance, integration of dR = 2cΓ2dt, assuming Γ ∝ R− (3−s)/2 according to the self–similar solution of Blandford & McKee (1976, hereafter BM76), we obtain a = 4−s.

G10 assume a relation between Γ and R which is formally identical to the BM76 solution but with a different normalisation factor: (6)where m(R) is the interstellar mass swept by the fireball up to the radius R.

In G10, the authors are interested in the determination of the peak time of the light curve, but that expression can also be used to determine the initial Lorentz factor Γ0. The expression, generalised for a power law profile of the external medium density, is: (7)This estimate of Γ0 is even lower than that of SP99 and, therefore, we can reasonably presume that also this value of Γ0 will be underestimated with respect to the real one8.

5.4. Ghirlanda et al. (2012)

Ghirlanda et al. (2012) derive another formula to estimate Γ0, based on the method proposed in G10, that is, intersecting the asymptotic behaviours of the bolometric light curve during the coasting phase with that during the deceleration phase. In order to describe Γ(R) in the deceleration regime, G12 use the BM76 solution with the correct (with respect to G10) normalisation factor: (8)The relation between radius and time is that presented in G10: (9)where t ≪ tp,z (t ≫ tp,z) corresponds to the coasting (deceleration) phase. The authors use these relations to obtain analytically the bolometric light curves before and after the peak and extrapolate them to get the intersection time which is used to infer Γ0: (10)The main difference with respect to the formula of G10 comes from the normalisation factor of the BM76 solution and corresponds to a factor [(17−4s)/(12−4s)] 1/2.

5.5. Nava et al. (2013)

Nava et al. (2013, hereafter N13) propose a new model to describe the dynamic evolution of the fireball during the afterglow emission. With this model, valid for an adiabatic or full and semi–radiative regime, N13 compute the bolometric afterglow light curves and derive a new analytic formula for Γ0. They rely on the same method (intersection of coasting/deceleration phase luminosity solution) already used by G10 and G12, but with a more realistic description of the dynamics of the fireball, provide an analytic formula for the estimate of Γ0 in the case of a purely adiabatic evolution (Eq. (11)), and a set of numerical coefficients to be used for the full or semi–radiative evolution.

In the rest of the present work we will adopt the formula of N13 to compute Γ0 so we report here their equation: (11)The difference with respect to the formula of G12 is due to the different relation between the shock radius R and the observed time t. All the preceding derivations assume that most of the observed emission comes from the single point of the expanding fireball that is moving exactly toward the observer, along the line of sight. Actually, the emitted radiation that arrives at time t to the observer does not come from a single point, but rather from a complex surface (Equal Arrival Time Surface, EATS) that does not coincide with the surface of the shock front. N13 avoid the rather complex computation of the EATSs, adopting a relation t(R) proposed by Waxman (1997) to relate radii to times. For simplicity, in the ultra–relativistic approximation: (12)where Γsh = 2Γ is the shock Lorentz factor. The time is the sum of a radial time tR, that is the delay of the shock front with respect to the light travel time at radius R and an angular time tθ, that is the delay of photons emitted at the same radius R but at larger angles with respect to the line of sight. Using the correct dynamics, N13 obtain the relation between observed time and shock front radius: (13)N13 use these relations to obtain analytically the bolometric light curve during the coasting and the deceleration phase and, from their intersection time, estimate Γ0 through Eq. (11).

Table 2

Comparison between the different methods of estimating Γ0 found in literature.

5.6. Nappo et al. (2014)

Nappo et al. (2014, hereafter N14) do not introduce directly a new formula to estimate Γ0, but show a new way (valid only in the ultra–relativistic regime Γ ≫ 1) to convert the shock radius R into the observer time t, bypassing the problem of the calculation of the EATSs. They assume that most of the observed emission is produced in a ring with aperture angle sinθ = 1/Γ around the line of sight. The differential form of the relation between observed time and radius can be written as dR = cΓ2dt, that differs from the analogous relations of G10 and G12 by a factor 2.

Using this simplified relation coupled to the dynamics of N13 we derive a new expression for Γ0: (14)We show in the following paragraph that this expression provides results that are very similar to those obtained with the formula of N13, proving that the approximation on the observed times is compatible with that suggested by N13 and before by Waxman (1997).

5.7. Comparison between different methods

All the previous expressions have the same dependencies on the values of E0, n0, and tp,z. They differ only by a numeric factor and can be summarised in one single expression: (15)where k is a numeric factor that depends on the chosen method and on the power law index of the external medium density profile. In Table 2 we list the different values of k for the various models and we show the comparison between the different estimates of Γ0 for both a homogeneous and a wind medium. All the possible estimates of Γ0 are within a factor ~ 2 of the estimate of N13; in particular the value provided by the N14 formula (Eq. (14)) is similar to N13 within a few percent.

thumbnail Fig. 6

Cumulative distribution of Γ0 for GRBs with measured tp (red solid line). The distribution of lower limits , derived for GRBs with an upper limit on the onset time , is shown by the dashed black curve (with rightward arrows). Assuming tp ≥ Tp,γ the distribution of upper limits on Γ0 is shown by the dotted grey line (with leftward arrows). The most stringent limit on the distribution of Γ0 is shown by the green solid line which assumes that deceleration radius Rdec ≥ R(τ = 1), that is, the transparency radius (Eq. (16)). Joining estimates of Γ0 and lower limits the reconstructed (through the KM estimator) distribution of Γ0 is shown by the solid black line (and its 95% uncertainty by the yellow shaded region). The distributions are normalised to their respective number of elements. The two panels (right and left, respectively) show the case of a homogeneous (s = 0) and wind (s = 2) medium.

6. Results

Through Eq. (11) we estimate:

the bulk Lorentz factors Γ0 of the 67 GRBs with measured tp;

lower limits for the 85 bursts with upper limits on the onset time ;

upper limit for the 85 bursts with lower limits on the onset time .

We consider both a homogeneous density ISM (s = 0) and a wind density profile (s = 2). For the first case, we assume n0 = 1 cm-3. For the second case, n(r) = n0r-2 = Ṁ/ 4πr2mpvw where Ṁ is the mass-loss rate and vw the wind velocity. For typical values (e.g. Chevalier & Li 1999) Ṁ = 10-5M⊙ yr-1 and vw = 103 km s-1, the normalisation of the wind case is cm-1.

In both cases we assume that the radiative efficiency of the prompt phase is η = 20% and estimate the kinetic energy of the blast wave in Eq. (11) as E0 = Eiso/η. The assumed typical value for η is similar to that reported in Nava et al. (2014) and Beniamini et al. (2015) who also find a small scatter of this parameter. We notice that assuming different values of n0 and η within a factor of 10 and 3 with respect to those adopted in our analysis would introduce a systematic difference in the estimate of Γ0 corresponding to a factor ~1.5 (2.3) for s = 0(s = 2).

6.1. Distribution of Γ0

Figure 6 shows the cumulative distribution of Γ0. The solid red line is the distribution of Γ0 for the 66 GRBs with a measure of tp. The cumulative distribution of is shown by the black dashed line (with rightward arrows) in Fig. 6. The cumulative distribution of is shown by the dotted grey line (with leftward arrows) in Fig. 6. We note that while is derived from the optical light curves decaying without any sign of the onset (i.e. providing ), the limit is derived assuming that the onset time happens after the peak of the prompt emission (i.e. Tp,γ).

A theoretical upper limit on Γ0 can be derived from the the transparency radius, that is, R(τ = 1) (Daigne & Mochkovitch 2002). The maximum bulk Lorentz factor attainable, if the acceleration is due to the internal pressure of the fireball (i.e. R ∝ Γ) is: (16)where σT is the Thomson cross section and R0 is the radius where the fireball is launched. We assume R0 ~ 108 cm. This is consistent with the value obtained from the modelling of the photospheric emission in a few GRBs Ghirlanda et al. (2013b). The cumulative distribution of upper limits on Γ0 obtained through Eq. (16), substituting for each GRB its Liso, is shown by the green solid line in Fig. 6 (with the leftward green arrows). This distribution represents the most conservative limit on Γ09.

Table 3

Average values and 68% confidence intervals on Γ0 (derived with only measured tp – red line in Fig. 6) or including lower limits (derived from upper limits – black line in Fig. 6).

thumbnail Fig. 7

Correlation between the isotropic equivalent luminosity Liso and the bulk Lorentz factor Γ0. Estimates of Γ0 from the measured afterglow onset tp for 68 long GRBs (red filled circles and green filled stars) and the short GRB 090510 (green filled square symbol). Lower limits (derived from upper limits on the onset time ) are shown by the rightward black arrows. Upper limits (derived assuming that the afterglow onset time is larger than the peak time of the prompt emission, that is, tp ≥ Tp,γ) are shown by the grey (leftward) arrows. Upper limit on Γ0 imposed by the requirement that the deceleration radius is larger than the transparency radius is shown by the grey shaded triangular region (for two values of the radius R0 where the fireball is formed – see Eq. (16)). The solid red line shows the correlation obtained with Γ0 solely (bisector method), while the black dot–dashed line is the correlation obtained through the Monte Carlo method which accounts for the reconstructed distribution of Γ0. Left and right panels show the case of a homogeneous and wind medium, respectively.

Similarly to the cumulative distributions of tp, shown in Fig. 1, also the distributions of Γ0 (red solid line) and the distribution of lower limits (black dashed line) are very close to each other. While at low and high values of Γ0 the two curves are consistent with one another, for intermediate values of Γ0 the distribution of lower limits is very close to that of measured Γ0. In the wind case (right panel of Fig. 6), the lower limits distribution violates the distribution of measured Γ0. This suggests that the distribution of Γ0 obtained only with measured tp suffers from the observational bias related to the lack of GRBs with very early optical observations.

We used the KM estimator to reconstruct the distribution of Γ0, combining measurements and lower limits, similarly to what has been done in Sect. 3.1 for tp. The solid black line (with its 95% uncertainty) in Fig. 6 shows the most likely distribution of Γ0 for the population of long GRBs under the assumption of a homogeneous ISM (left panel) and for a wind medium (right panel).

The median values of Γ0 (reported in Table 3) are 320 and 150 for the homogeneous and wind case, respectively, and they are consistent within their 1σ confidence intervals. G12 found smaller average values of Γ0 (i.e. 138 and 66 in the homogeneous and wind case, respectively) because of the smaller sample size (30 GRBs) and the non-inclusion of limits on Γ0. Indeed, while the intermediate/small values of Γ0 are reasonably well sampled by the measurements of tp, the bias against the measure of large Γ0 is due to the lack of small tp measurements (the smallest tp are actually provided by the still few LAT detections).

The reconstructed distribution of Γ0 (black line in Fig. 6) is consistent with the distribution of upper limits derived assuming tp ≥ Tp,γ (dotted grey distribution) in the homogeneous case. For the wind medium there could be a fraction of GRBs (~20%) whose tp is smaller than the peak of the prompt emission. However, Fig. 6 shows that, both in the homogeneous and wind case, the reconstructed Γ0 distribution is consistent with the limiting distribution (green line) derived assuming that the deceleration occurs after transparency is reached.

7. Correlations

G12 found correlations between the bulk Lorentz factor Γ0 and the prompt emission properties of GRBs: , , and with a larger scatter, Ep ∝ Γ0. Interestingly, combining these correlations leads to and which are the Ep−Eiso (Amati et al. 2002) and Yonetoku (Yonetoku et al. 2004) correlations. G12 showed that, in order to reproduce also the Ep−Eγ correlation (Ghirlanda et al. 2007), the bulk Lorentz factor and the jet opening angle should be In this section, with the 66 long GRBs with measured Γ0 (a factor ~3 larger sample than that used in G12) plus 85 lower/upper limits, we analyse the correlations of Γ0 (both in the homogeneous and wind case) with Liso, Eiso, and Ep.

thumbnail Fig. 8

Correlation between the bulk Lorentz factor Γ0 and the isotropic equivalent energy Eiso. Same symbols as Fig. 7.

thumbnail Fig. 9

Correlation between the bulk Lorentz factor Γ0 and the peak energy Ep. Same symbols as Fig. 7.

Table 4

Correlations between Liso, Eiso, and Ep with Γ0.

Figure 7 shows the correlation between Γ0 (for the homogeneous and wind density circumburst medium, left and right panels, respectively) and Liso. Lower limits are shown by rightward black arrows and occupy the same region of the data points with estimated Γ0 (red symbols). The green symbols show the LAT bursts which have the largest values of Liso and Γ0. Upper limits obtained requiring that the onset of the afterglow happens after the main emission peak of the prompt light curve are shown by the grey (leftward) arrows.

The shadowed grey region shown in Fig. 7 shows the limit obtained requiring that the deceleration happens after the transparency radius, that is, Rdec ≥ Rth. Two different limits are shown corresponding to different (by a factor of 10) assumptions for R0, that is, the radius where the jet is launched (see Eq. (16)). The correlation between Liso and Γ0 in the wind profile (bottom panel of Fig. 7) is less scattered than in the homogeneous case (upper panel of Fig. 7). The correlations between Γ0 and the isotropic energy Eiso and the peak energy Ep are shown in Figs. 8 and 9, respectively. Symbols are the same as in Fig. 7. Also in these cases, the correlation in the wind case is less scattered than in the homogeneous case.

The correlations shown in Figs. 7–9 were analysed computing the Spearman’s rank correlation coefficient r and its probability P and fitting the data, with the bisector method, with a linear model: (17)where Y is either Liso or Eiso. Similarly, for the correlation between Ep and Γ0 we adopted the model: (18)The results are given in Table 4 both for the homogeneous and for the wind case. Correlation analysis considering only estimates of Γ0 are shown by the red solid line in Figs. 7–9.

In order to reconstruct the correlation considering measured Γ0 and upper/lower limits, we adopted the same Monte Carlo procedure described in Sect. 3. We generate 105 samples composed by the GRBs with measured Γ0 and assigning to the 85 GRBs without measured tp a value of Γ0 randomly extracted from the reconstructed distribution of Γ0 shown in Fig. 6. We then analyse the correlations of Γ0 and the energetic variables within these random samples and report the central values of the correlation parameters (coefficient, probability, slope and normalisation) in Table 4. In all cases we find significant correlations also when upper/lower limits are accounted for with this Monte Carlo method. The corresponding correlation lines are shown with the dot–dashed lines in Figs. 7–9. We note that the correlations found with only Γ0 or reconstructed accounting also for limits are very similar.

Figure 7 (and similarly Figs. 8 and 9) shows that the planes are not uniformly filled with data points, contrary to what is claimed by Hascoët et al. (2014). Indeed, the right part of the planes of Figs. 7 and 8 corresponding to large Γ0 and any possible value of Liso and Eiso, respectively, are limited by the excluded region, that is, deceleration should happen after the fireball transparency. Moreover, the upper limits obtained requiring that the afterglow onset time tp is after the main prompt emission peak leads to the upper limits shown by the grey downward arrows in Figs. 7 and 8, which are even more constraining than the shaded regions. This confirms that the bulk Lorentz factor is indeed strongly correlated with the prompt emission properties (Eiso, Liso, and Ep).

thumbnail Fig. 10

Distribution of comoving frame properties of GRBs. Left panel: comoving frame isotropic luminosity (back and grey histogram for the homogeneous and wind case, respectively) and of the isotropic energy (red and orange histogram for the homogeneous and wind case, respectively). Right panel: comoving frame peak energy (black and grey histogram for the homogeneous and wind case, respectively).

Table 5

Average values and width of the distribution of the (log values of) Eiso, Liso, and Ep, in the rest frame and in the comoving frame (both for the homogeneous and the wind density profile).

We compute the comoving frame peak energy and isotropic energy with the equations derived in G12: and . The luminosity . Primed quantities refer to the comoving frame. For GRBs with a a lower limit transforms into an upper limit on , , and . Vice versa, lower limits provide upper limits which transform the rest frame observables into lower limits in the comoving frame. Through the Monte Carlo method adopted in the previous sections we derive the distributions of the comoving frame , , and ; these are shown in Fig. 10. The average values of Γ0 for the homogeneous ISM is larger than that of the wind case. For this reason the distributions of the comoving frame quantities shown in Fig. 10 are slightly shifted in the two scenarios with the homogeneous case resulting in a slightly smaller average value of the comoving frame isotropic energy, luminosity, and peak energy. The average values of the comoving frame distributions shown in Fig. 10 are reported in Table 5.

8. Summary

The present work assembles the largest sample of tp, by revising and expanding (to June 2016) the original sample of G12, and including upper limits corresponding to GRBs without a measured onset time. Our sample, presented in Table A.1, is composed of:

67 GRBs with measured tp: 66 long and one short (GRB 090510). Eight tp are measured from the GeV light curve.

106 long GRBs with an upper limit . These are GRBs detected in the optical or GeV band within one day and showing a decaying light curve. Five are measured from the GeV light curve.

An upper limit gives a lower limit according to equations in Sect. 4. Accounting only for the most stringent upper limits, we consider only the 85 bursts with s, corresponding to five times the maximum tp among the 66 GRBs. Therefore, the final sample is composed of 151 GRBs: 66 long GRBs10 with tp and 85 GRBs with .

The observable is tp: Fig. 1 shows that the relative position of the cumulative distribution of tp (red line) and of lower limits (dashed line) suggests the presence of a selection bias against the measurement of intermediate/small tp values. The earliest tp are provided by the few GRBs with an onset measured from the GeV light curve by the LAT on board Fermi.

We can extract more information from the sample of tp if we use also upper limits . Statistical methods (e.g. Feigelson & Nelson 1985) allow us to reconstruct the distribution of an observable adopting measurements and upper limits, provided upper limits cover the same range of values of detections. This is our case as shown in Fig. 1. We adopt the Kaplan-Meier estimator to reconstruct the distribution of tp of the population of long GRBs. We find that:

The reconstructed distribution of tp (solid black line with yellow shaded region in Fig. 1) has median value ⟨ tp ⟩ ~ 60 s extending from a few seconds (tp from LAT) to relatively late tp ~ 103 s; in the rest frame, the average tp/ (1 + z) is 20 s.

The tp distribution is consistent with the cumulative distribution of Tp,γ (dot–dashed grey line in Fig. 1). Tp,γ is the time when the prompt emission light curve peaks.

The rest frame tp/ (1 + z) is inversely correlated with Eiso, Liso and Ep (Fig. 4). These correlations are statistically significant (Table 1).

Since , an upper limit on tp provides a lower limit . We combine Γ0 and finding that:

The reconstructed distribution of Γ0 (solid black line and yellow shaded region in Fig. 6) has a median of ⟨ Γ0 ⟩ = 320 and 155 in the homogeneous and wind case, respectively (Table 3).

Γ0 values span two orders of magnitude from 20 to 1000 in the wind case (right panel in Fig. 6) and a slightly smaller range in the homogeneous case (left panel in Fig. 6).

The distribution of Γ0 is consistent with the distribution of upper limits derived under the assumption that the afterglow peak is larger than the peak of the prompt emission (i.e. tp>Tp,γ – dotted grey line in Fig. 6).

thumbnail Fig. 11

Distribution of the mass of the jet (in solar masses) for the homogeneous case (red solid line) and for the wind case (blue solid line) computed assuming a typical jet opening angle of 5 degrees.

Through a Monte Carlo method we combine the 66 tp with the 85 GRBs with . In the latter cases Γ0 should lie in the range (where and are obtained from and Tp,γ). We assign values of Γ0, randomly extracted from the reconstructed distribution and requiring they are comprised within the above limits, to the 85 GRBs. Creating 105 mock samples of 66 GRBs with measured Γ0 plus the randomly assigned 85 values, we evaluate the various dependencies of this parameter on the prompt emission properties analysing these random samples:

There are significant correlations between Γ0 and the prompt emission rest frame properties, namely Eiso, Liso, and Ep (shown in Figs. 7, 8, and 10 and Table 4): where H and W denote the homogeneous and wind case, respectively. The correlations are less scattered in the W case.

The distribution of the data points and upper/lower limits in Figs. 7–9 show that these planes are not uniformly filled with data.

There is no correlation between the rest frame duration T90/ (1 + z) and Γ0.

With the distribution of bulk Lorentz factors reconstructed from our sample of GRBs, we can compute the baryon loading of the fireball M = Ek/ Γ0c2, where Ek = Eiso/η is the kinetic energy of the blast wave. This would lead to the isotropic equivalent mass loading but, since GRBs have a jet, we derive the baryon loading of the jet assuming a typical opening angle of 5 degrees (e.g. Ghirlanda et al. 2007). Figure 11 shows the distribution of the jet baryon loading in units of solar masses obtained in the homogeneous (red line) and wind (blue line) case. The baryon loading is distributed around a typical value of a few 10-6M⊙ which is similar in the two density scenarios.

9. Conclusions

We have extended the sample of GRBs with measured onset time tp including for the first time also upper limits tp. While the onset time happens relatively early (the mean value of the distribution, accounting for upper limits, is ~60 s in the observer frame), in most cases this happens after the time of the peak of the prompt emission of GRBs. This ensures that a considerable fraction of the fireball kinetic energy should have been transferred to the blast wave whose deceleration produces tp. Therefore, the so called “thin shell approximation” (i.e. the requirement that most of the fireball energy has been transferred to the blast wave that decelerates into the interstellar medium) adopted in deriving Γ0 from the measure of tp holds. This regime is only partly established in jets that remain highly magnetised at the afterglow stage (Mimica et al. 2009) and seems to support a low magnetisation outflow. Moreover, we have shown that the synchrotron characteristic frequency νinj, computed at tp, is above the optical R band (Fig. 5) for all bursts. This ensures that it cannot be responsible for the peak of the afterglow light curve sweeping through the observer frame optical band. This allows us to interpret tp as the fireball deceleration onset time and use it to compute the bulk Lorentz factor Γ0.

We have reviewed and compared the different methods and formulae proposed in the literature (Ghirlanda et al. 2012; Ghisellini et al. 2010; Molinari et al. 2007; Nappo et al. 2014; Nava et al. 2013; Sari & Piran 1999) to compute Γ0 from tp. They differ at most by a factor of two. Therefore, any different choice of the specific formula to compute Γ0 should only introduce a small systematic difference in the derived correlation normalisation and average values.

The average Γ0 is 320 and 155, for the homogeneous and wind cases, respectively. These values are larger than those found in G12 due to our larger sample (almost a factor 2) and to the inclusion of lower limits . We confirm the existence of significant correlations between the GRB bulk Lorentz factor and the prompt emission observables, that is, isotropic energy and luminosity and peak energy (Eq. (21)). The Γ0−Liso, Γ0−Eiso, and Γ0−Ep correlations are not boundaries in these planes (opposite to what claimed by Hascoët et al. 2014). With respect to the sample considered in Hascoët et al. (2014), our samples of tp and are a factor three and four larger, respectively.

By combining these correlations, we find and in the homogenous case (and and in the wind case). These slopes are consistent with the Ep−Eiso and Ep−Liso correlations (Amati et al. 2002; Yonetoku et al. 2004) that we have re-derived here through our sample of 151 GRBs, and are and , respectively.

Finally, the knowledge of Γ0 allows us to derive the mass of the fireball for individual GRBs. Assuming a typical jet opening angle of 5 degrees, the jet mass Mjet is similarly distributed for the homogeneous and wind cases (red and blue lines, respectively, in Fig. 11) between 10-8 and 10-4M⊙.

1

Liang et al. (2010), Lü et al. (2012) and Wu et al. (2011) include in their samples also bursts with a peak in the X-ray light curve thus resulting in a larger but less homogeneous and secure sample of tp.

2

In these events Γ0 might still be estimated, but only under some assumptions on the dynamical evolution of the burst outflow (e.g. GRB 090124 – Nappo et al. 2014).

3

The short GRB 090510 is not included in the distributions. Its onset time tp = 0.2 s Ghirlanda et al. (2010) would place it in the lowest bin of the distribution.

4

We note also that for the LAT bursts (star symbols) the injection frequency is a factor 10 below the GeV band, and also in these cases the peak of the LAT light curve can be interpreted as the deceleration peak (Nava et al. 2017).

5

On the other hand, as discussed in G12 and Nappo et al. (2014), there are ways to obtain a peak also in the s = 2 case. This is why we consider both the homogeneous and the wind case for all bursts. Observationally, most early light curves indeed show a peak.

6

Liang et al. (2010) adopt the same equation as M07.

7

G10 derive tpeak in the case of an adiabatic or a fully radiative evolution of the fireball that propagates in a homogeneous medium. For our purposes we consider only the adiabatic case.

8

Although Eq. (7) is obtained with an incorrect normalisation of the relation between Γ and R, we report also this derivation since it was the first to propose a different method to derive the time of the peak of the afterglow. Cfr. with Eq. (8) showing the correct normalisation.

9

Changing the assumed value of R0 within a factor of 10 shifts the green curve by a factor ~1.8.

10

In most of the Figures we show for comparison also the short GRB 090510: this is not included in the quantitive analysis.

Acknowledgments

INAF PRIN 2014 (1.05.01.94.12) is acknowledged. The anonymous referee is acknowledged for useful comments that helped to improve the content of the paper.

References

  1. Abdo, A. A., Ackermann, M., Arimoto, M., et al. 2009a, Science, 323, 1688 [Google Scholar]
  2. Abdo, A. A., Ackermann, M., Asano, K., et al. 2009b, ApJ, 707, 580 [NASA ADS] [CrossRef] [Google Scholar]
  3. Ackermann, M., Ajello, M., Baldini, L., et al. 2010a, ApJ, 717, L127 [NASA ADS] [CrossRef] [Google Scholar]
  4. Ackermann, M., Asano, K., Atwood, W. B., et al. 2010b, ApJ, 716, 1178 [NASA ADS] [CrossRef] [Google Scholar]
  5. Ackermann, M., Ajello, M., Asano, K., et al. 2013, ApJS, 209, 11 [NASA ADS] [CrossRef] [Google Scholar]
  6. Ackermann, M., Ajello, M., Asano, K., et al. 2014, Science, 343, 42 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  7. Akerlof, C. W., Kehoe, R. L., McKay, T. A., et al. 2003, PASP, 115, 132 [NASA ADS] [CrossRef] [Google Scholar]
  8. Amati, L., Frontera, F., Tavani, M., et al. 2002, A&A, 390, 81 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Bardho, O., Gendre, B., Rossi, A., et al. 2016, MNRAS, 459, 508 [NASA ADS] [CrossRef] [Google Scholar]
  10. Baring, M. G., & Harding, A. K. 1997, ApJ, 491, 663 [Google Scholar]
  11. Beniamini, P., & van der Horst, A. J. 2017, MNRAS, 472, 3161 [NASA ADS] [CrossRef] [Google Scholar]
  12. Beniamini, P., Nava, L., Duran, R. B., & Piran, T. 2015, MNRAS, 454, 1073 [NASA ADS] [CrossRef] [Google Scholar]
  13. Beniamini, P., Nava, L., & Piran, T. 2016, MNRAS, 461, 51 [NASA ADS] [CrossRef] [Google Scholar]
  14. Berger, E., Kulkarni, S. R., Bloom, J. S., et al. 2002, ApJ, 581, 981 [NASA ADS] [CrossRef] [Google Scholar]
  15. Bhalerao, V. B., Singer, L. P., Kasliwal, M. M., et al. 2014, GRB Coordinates Network, 16442 [Google Scholar]
  16. Blandford, R. D., & McKee, C. F. 1976, Phys. Fluids, 19, 1130 [NASA ADS] [CrossRef] [Google Scholar]
  17. Bloom, J. S., Perley, D. A., Li, W., et al. 2009, ApJ, 691, 723 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  18. Breeveld, A. A., & Kuin, N. P. M. 2013, GRB Coordinates Network, 14957 [Google Scholar]
  19. Buckley, D., Potter, S., Kniazev, A., et al. 2014, GRB Coordinates Network, 17237 [Google Scholar]
  20. Burd, A., Cwiok, M., Czyrkowski, H., et al. 2005, New Astron., 10, 409 [NASA ADS] [CrossRef] [Google Scholar]
  21. Burgess, J. M., Bégué, D., Ryde, F., et al. 2016, ApJ, 822, 63 [NASA ADS] [CrossRef] [Google Scholar]
  22. Butler, N. R., Kocevski, D., Bloom, J. S., & Curtis, J. L. 2007, ApJ, 671, 656 [NASA ADS] [CrossRef] [Google Scholar]
  23. Butler, N. R., Bloom, J. S., & Poznanski, D. 2010, ApJ, 711, 495 [NASA ADS] [CrossRef] [Google Scholar]
  24. Cenko, S. B., & Perley, D. A. 2015, GRB Coordinates Network, 17612 [Google Scholar]
  25. Cenko, S. B., Urban, A. L., Perley, D. A., et al. 2015, ApJ, 803, L24 [NASA ADS] [CrossRef] [Google Scholar]
  26. Covino, S., D’Avanzo, P., Klotz, A., et al. 2008, MNRAS, 388, 347 [NASA ADS] [CrossRef] [Google Scholar]
  27. Cucchiara, A., Cenko, S. B., Bloom, J. S., et al. 2011, ApJ, 743, 154 [NASA ADS] [CrossRef] [Google Scholar]
  28. Daigne, F., & Mochkovitch, R. 2002, MNRAS, 336, 1271 [NASA ADS] [CrossRef] [Google Scholar]
  29. de Pasquale, M., & Cannizzo, J. K. 2013, GRB Coordinates Network, 14573 [Google Scholar]
  30. Denisenko, D., Gorbovskoy, E., Lipunov, V., et al. 2012, GRB Coordinates Network, 13623 [Google Scholar]
  31. Diercks, A. H., Deutsch, E. W., Castander, F. J., et al. 1998, ApJ, 503, L105 [NASA ADS] [CrossRef] [Google Scholar]
  32. Feigelson, E. D., & Nelson, P. I. 1985, ApJ, 293, 192 [NASA ADS] [CrossRef] [Google Scholar]
  33. Fenimore, E. E., Epstein, R. I., & Ho, C. 1993, A&AS, 97, 59 [NASA ADS] [Google Scholar]
  34. Ferrero, A., Hanlon, L., Felletti, R., et al. 2010, Adv. Astron., 2010, 715237 [NASA ADS] [CrossRef] [Google Scholar]
  35. Filgas, R., Greiner, J., Schady, P., et al. 2012, A&A, 546, A101 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  36. Frail, D. A., Kulkarni, S. R., Nicastro, L., Feroci, M., & Taylor, G. B. 1997, Nature, 389, 261 [NASA ADS] [CrossRef] [Google Scholar]
  37. Fukushima, T., To, S., Asano, K., & Fujita, Y. 2017, ApJ, 844, 92 [NASA ADS] [CrossRef] [Google Scholar]
  38. Fynbo, J. U., Gorosabel, J., Dall, T. H., et al. 2001, A&A, 373, 796 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. Galama, T. J., Briggs, M. S., Wijers, R. A. M. J., et al. 1999, Nature, 398, 394 [NASA ADS] [CrossRef] [Google Scholar]
  40. Gendre, B., Klotz, A., Palazzi, E., et al. 2010, MNRAS, 405, 2372 [NASA ADS] [Google Scholar]
  41. Genet, F., Daigne, F., & Mochkovitch, R. 2007, MNRAS, 381, 732 [NASA ADS] [CrossRef] [Google Scholar]
  42. Ghirlanda, G., Nava, L., Ghisellini, G., & Firmani, C. 2007, A&A, 466, 127 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  43. Ghirlanda, G., Ghisellini, G., & Nava, L. 2010, A&A, 510, L7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Ghirlanda, G., Nava, L., Ghisellini, G., et al. 2012, MNRAS, 420, 483 [NASA ADS] [CrossRef] [Google Scholar]
  45. Ghirlanda, G., Ghisellini, G., Salvaterra, R., et al. 2013a, MNRAS, 428, 1410 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Ghirlanda, G., Pescalli, A., & Ghisellini, G. 2013b, MNRAS, 432, 3237 [NASA ADS] [CrossRef] [Google Scholar]
  47. Ghisellini, G., Ghirlanda, G., Nava, L., & Firmani, C. 2007, ApJ, 658, L75 [NASA ADS] [CrossRef] [Google Scholar]
  48. Ghisellini, G., Nardini, M., Ghirlanda, G., & Celotti, A. 2009, MNRAS, 393, 253 [NASA ADS] [CrossRef] [Google Scholar]
  49. Ghisellini, G., Ghirlanda, G., Nava, L., & Celotti, A. 2010, MNRAS, 403, 926 [Google Scholar]
  50. Goodman, J. 1986, ApJ, 308, L47 [NASA ADS] [CrossRef] [Google Scholar]
  51. Gorbovskoy, E., Krushinski, V., Pruzhinskaya, M., et al. 2014a, GRB Coordinates Network, 16500 [Google Scholar]
  52. Gorbovskoy, E., Lipunov, V., Kornilov, V., et al. 2014b, GRB Coordinates Network, 16307 [Google Scholar]
  53. Gorbovskoy, E., Lipunov, V., Pruzhinskaya, M., et al. 2014c, GRB Coordinates Network, 15932 [Google Scholar]
  54. Gorbovskoy, E. S., Lipunov, V. M., Buckley, D. A. H., et al. 2016, MNRAS, 455, 3312 [NASA ADS] [CrossRef] [Google Scholar]
  55. Gorosabel, J., Mottola, S., de Ugarte Postigo, A., Hellmich, S., & Proffe, G. 2013, GRB Coordinates Network, 15469 [Google Scholar]
  56. Gorosabel, J., Hellmich, S., de Ugarte Postigo, A., Mottola, S., & Thoene, C. 2014, GRB Coordinates Network, 17197 [Google Scholar]
  57. Graff, P., Feroz, F., Hobson, M. P., & Lasenby, A. 2014, MNRAS, 441, 1741 [NASA ADS] [CrossRef] [Google Scholar]
  58. Granot, J., & van der Horst, A. J. 2014, PASA, 31, e008 [NASA ADS] [CrossRef] [Google Scholar]
  59. Greiner, J., Krühler, T., Fynbo, J. P. U., et al. 2009, ApJ, 693, 1610 [NASA ADS] [CrossRef] [Google Scholar]
  60. Groot, P. J., Galama, T. J., Vreeswijk, P. M., et al. 1998, ApJ, 502, L123 [NASA ADS] [CrossRef] [Google Scholar]
  61. Grupe, D., Brown, P. J., Cummings, J., et al. 2006, ApJ, 645, 464 [NASA ADS] [CrossRef] [Google Scholar]
  62. Guidorzi, C., Steele, I. A., Melandri, A., et al. 2009, GRB Coordinates Network, 9375 [Google Scholar]
  63. Halpern, J. P., Uglesich, R., Mirabal, N., et al. 2000, ApJ, 543, 697 [NASA ADS] [CrossRef] [Google Scholar]
  64. Hascoët, R., Daigne, F., Mochkovitch, R., & Vennin, V. 2012, MNRAS, 421, 525 [NASA ADS] [Google Scholar]
  65. Hascoët, R., Beloborodov, A. M., Daigne, F., & Mochkovitch, R. 2014, ApJ, 782, 5 [NASA ADS] [CrossRef] [Google Scholar]
  66. Henden, A., Gross, J., Denny, B., Terrell, D., & Cooney, W. 2009, GRB Coordinates Network, 9211 [Google Scholar]
  67. Holland, S., Fynbo, J. P. U., Hjorth, J., et al. 2001, A&A, 371, 52 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  68. Holland, S. T., Soszyński, I., Gladders, M. D., et al. 2002, AJ, 124, 639 [NASA ADS] [CrossRef] [Google Scholar]
  69. Ioka, K., Toma, K., Yamazaki, R., & Nakamura, T. 2006, A&A, 458, 7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  70. Isobe, T., Feigelson, E. D., & Nelson, P. I. 1986, ApJ, 306, 490 [NASA ADS] [CrossRef] [Google Scholar]
  71. Isobe, T., Feigelson, E. D., Akritas, M. G., & Babu, G. J. 1990, ApJ, 364, 104 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  72. Japelj, J., Kopac, D., Guidorzi, C., Mundell, C., & Virgili, F. 2012, GRB Coordinates Network, 14058 [Google Scholar]
  73. Japelj, J., Kopač, D., Kobayashi, S., et al. 2014, ApJ, 785, 84 [NASA ADS] [CrossRef] [Google Scholar]
  74. Jin, Z.-P., Covino, S., Della Valle, M., et al. 2013, ApJ, 774, 114 [NASA ADS] [CrossRef] [Google Scholar]
  75. Kasliwal, M. M., Cenko, S. B., & Singer, L. P. 2014, GRB Coordinates Network, 16425 [Google Scholar]
  76. King, O. G., Blinov, D., Giannios, D., et al. 2014, MNRAS, 445, L114 [NASA ADS] [CrossRef] [Google Scholar]
  77. Kinugasa, K., & Torii, K. 2004, GRB Coordinates Network, 2832 [Google Scholar]
  78. Klotz, A., Boër, M., Atteia, J. L., & Gendre, B. 2009, AJ, 137, 4100 [NASA ADS] [CrossRef] [Google Scholar]
  79. Klotz, A., Turpin, D., Atteia, J. L., et al. 2016, GRB Coordinates Network, 19622 [Google Scholar]
  80. Krimm, H. A., Granot, J., Marshall, F. E., et al. 2007, ApJ, 665, 554 [NASA ADS] [CrossRef] [Google Scholar]
  81. Krolik, J. H., & Pier, E. A. 1991, ApJ, 373, 277 [Google Scholar]
  82. Kruehler, T., Malesani, D., Xu, D., et al. 2014, GRB Coordinates Network, 15899 [Google Scholar]
  83. Krühler, T., Ledoux, C., Fynbo, J. P. U., et al. 2013, A&A, 557, A18 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  84. Kuin, N. P. M., & Yershov, V. N. 2012, GRB Coordinates Network, 13808 [Google Scholar]
  85. Kuin, P., de Pasquale, M., Malesani, D., & Brown, P. 2015, GRB Coordinates Network, 18224 [Google Scholar]
  86. Kumar, P., & Barniol Duran, R. 2010, MNRAS, 409, 226 [NASA ADS] [CrossRef] [Google Scholar]
  87. Kumar, P., & Zhang, B. 2015, Phys. Rep., 561, 1 [NASA ADS] [CrossRef] [Google Scholar]
  88. Lacluyze, A., Haislip, J., Ivarsen, K., et al. 2011, GRB Coordinates Network, 12535 [Google Scholar]
  89. Laskar, T., Alexander, K. D., Berger, E., et al. 2016, ApJ, 833, 88 [NASA ADS] [CrossRef] [Google Scholar]
  90. Lei, W.-H., Zhang, B., & Liang, E.-W. 2013, ApJ, 765, 125 [NASA ADS] [CrossRef] [Google Scholar]
  91. Li, W., Filippenko, A. V., Chornock, R., & Jha, S. 2003, ApJ, 586, L9 [NASA ADS] [CrossRef] [Google Scholar]
  92. Liang, E.-W., Yi, S.-X., Zhang, J., et al. 2010, ApJ, 725, 2209 [NASA ADS] [CrossRef] [Google Scholar]
  93. Liang, E.-W., Li, L., Gao, H., et al. 2013, ApJ, 774, 13 [Google Scholar]
  94. Lien, A. Y., Barthelmy, S., Maselli, A., & Oates, S. R. 2014, GCN Report, 466 [Google Scholar]
  95. Lipunov, V. M., Krylov, A. V., Kornilov, V. G., et al. 2004, Astron. Nachr., 325, 580 [NASA ADS] [CrossRef] [Google Scholar]
  96. Lipunov, V. M., Gorosabel, J., Pruzhinskaya, M. V., et al. 2016, MNRAS, 455, 712 [NASA ADS] [CrossRef] [Google Scholar]
  97. Lithwick, Y., & Sari, R. 2001, ApJ, 555, 540 [NASA ADS] [CrossRef] [Google Scholar]
  98. Liu, B., Chen, W., Liang, Y.-F., et al. 2014, ApJ, 787, L6 [NASA ADS] [CrossRef] [Google Scholar]
  99. Lü, J., Zou, Y.-C., Lei, W.-H., et al. 2012, ApJ, 751, 49 [NASA ADS] [CrossRef] [Google Scholar]
  100. Maiorano, E., Masetti, N., Palazzi, E., et al. 2006, A&A, 455, 423 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  101. Margutti, R., Guidorzi, C., Chincarini, G., et al. 2010, MNRAS, 406, 2149 [NASA ADS] [CrossRef] [Google Scholar]
  102. Marshall, F. E., & Cannizzo, J. K. 2015, GRB Coordinates Network, 17820 [Google Scholar]
  103. Marshall, F. E., vanden Berk, D. E., & Racusin, J. 2007, GRB Coordinates Network, 6041 [Google Scholar]
  104. Martin-Carrillo, A., Hanlon, L., Topinka, M., et al. 2014, A&A, 567, A84 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  105. Mazaeva, E., Klunko, E., Volnova, A., Korobtsev, I., & Pozanenko, A. 2015, GRB Coordinates Network, 18175 [Google Scholar]
  106. Melandri, A., Kobayashi, S., Mundell, C. G., et al. 2010, ApJ, 723, 1331 [NASA ADS] [CrossRef] [Google Scholar]
  107. Melandri, A., Covino, S., Rogantini, D., et al. 2014a, A&A, 565, A72 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  108. Melandri, A., Virgili, F. J., Guidorzi, C., et al. 2014b, A&A, 572, A55 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  109. Mesler, R. A., Pihlström, Y. M., Taylor, G. B., & Granot, J. 2012, ApJ, 759, 4 [NASA ADS] [CrossRef] [Google Scholar]
  110. Mimica, P., Giannios, D., & Aloy, M. A. 2009, A&A, 494, 879 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  111. Molinari, E., Vergani, S. D., Malesani, D., et al. 2007, A&A, 469, L13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  112. Nappo, F., Ghisellini, G., Ghirlanda, G., et al. 2014, MNRAS, 445, 1625 [NASA ADS] [CrossRef] [Google Scholar]
  113. Nardini, M., Ghisellini, G., Ghirlanda, G., & Celotti, A. 2010, MNRAS, 403, 1131 [NASA ADS] [CrossRef] [Google Scholar]
  114. Nardini, M., Elliott, J., Filgas, R., et al. 2014, A&A, 562, A29 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  115. Nava, L., Sironi, L., Ghisellini, G., Celotti, A., & Ghirlanda, G. 2013, MNRAS, 433, 2107 [NASA ADS] [CrossRef] [Google Scholar]
  116. Nava, L., Vianello, G., Omodei, N., et al. 2014, MNRAS, 443, 3578 [NASA ADS] [CrossRef] [Google Scholar]
  117. Nava, L., Desiante, R., Longo, F., et al. 2017, MNRAS, 465, 811 [NASA ADS] [CrossRef] [Google Scholar]
  118. Oates, S. R., & Hagen, L. M. Z. 2015, GRB Coordinates Network, 17424 [Google Scholar]
  119. Oates, S. R., & Racusin, J. L. 2012, GRB Coordinates Network, 13725 [Google Scholar]
  120. Paczynski, B. 1986, ApJ, 308, L43 [NASA ADS] [CrossRef] [Google Scholar]
  121. Page, K. L., Starling, R. L. C., Fitzpatrick, G., et al. 2011, MNRAS, 416, 2078 [NASA ADS] [CrossRef] [Google Scholar]
  122. Panaitescu, A. 2008, MNRAS, 383, 1143 [NASA ADS] [CrossRef] [Google Scholar]
  123. Panaitescu, A. 2017, ApJ, 837, 13 [NASA ADS] [CrossRef] [Google Scholar]
  124. Panaitescu, A., & Kumar, P. 2000, ApJ, 543, 66 [NASA ADS] [CrossRef] [Google Scholar]
  125. Park, H. S., Ables, E., Band, D. L., et al. 1997, ApJ, 490, 99 [NASA ADS] [CrossRef] [Google Scholar]
  126. Perley, D. A., Bloom, J. S., Butler, N. R., et al. 2008a, ApJ, 672, 449 [NASA ADS] [CrossRef] [Google Scholar]
  127. Perley, D. A., Li, W., Chornock, R., et al. 2008b, ApJ, 688, 470 [NASA ADS] [CrossRef] [Google Scholar]
  128. Pihlström, Y. M., Taylor, G. B., Granot, J., & Doeleman, S. 2007, ApJ, 664, 411 [NASA ADS] [CrossRef] [Google Scholar]
  129. Piran, T. 1999, Phys. Rep., 314, 575 [NASA ADS] [CrossRef] [Google Scholar]
  130. Price, P. A., Kulkarni, S. R., Berger, E., et al. 2003, ApJ, 589, 838 [NASA ADS] [CrossRef] [Google Scholar]
  131. Racusin, J. L., Karpov, S. V., Sokolowski, M., et al. 2008, Nature, 455, 183 [Google Scholar]
  132. Romano, P., Campana, S., Chincarini, G., et al. 2006, A&A, 456, 917 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  133. Rossi, A., Schulze, S., Klose, S., et al. 2011, A&A, 529, A142 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  134. Rykoff, E. S., Aharonian, F., Akerlof, C. W., et al. 2009, ApJ, 702, 489 [NASA ADS] [CrossRef] [Google Scholar]
  135. Sahu, K. C., Vreeswijk, P., Bakos, G., et al. 2000, ApJ, 540, 74 [NASA ADS] [CrossRef] [Google Scholar]
  136. Sakamoto, T., Barthelmy, S. D., Baumgartner, W. H., et al. 2011, ApJS, 195, 2 [NASA ADS] [CrossRef] [Google Scholar]
  137. Salvaterra, R., Della Valle, M., Campana, S., et al. 2009, Nature, 461, 1258 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  138. Salvaterra, R., Campana, S., Vergani, S. D., et al. 2012, ApJ, 749, 68 [NASA ADS] [CrossRef] [Google Scholar]
  139. Sánchez-Ramírez, R., Hancock, P. J., Jóhannesson, G., et al. 2017, MNRAS, 464, 4624 [NASA ADS] [CrossRef] [Google Scholar]
  140. Santana, R., Barniol Duran, R., & Kumar, P. 2014, ApJ, 785, 29 [NASA ADS] [CrossRef] [Google Scholar]
  141. Sari, R., & Piran, T. 1999, ApJ, 520, 641 [NASA ADS] [CrossRef] [Google Scholar]
  142. Sari, R., Piran, T., & Narayan, R. 1998, ApJ, 497, L17 [NASA ADS] [CrossRef] [Google Scholar]
  143. Sato, R., Kawai, N., Suzuki, M., et al. 2003, ApJ, 599, L9 [NASA ADS] [CrossRef] [Google Scholar]
  144. Siegel, M. H., & Gompertz, B. P. 2014, GRB Coordinates Network, 16219 [Google Scholar]
  145. Singer, L. P., Cenko, S. B., Kasliwal, M. M., Fremling, C., & Dzigan, Y. 2014, GRB Coordinates Network, 16226 [Google Scholar]
  146. Soderberg, A. M., Kulkarni, S. R., Price, P. A., et al. 2006, ApJ, 636, 391 [NASA ADS] [CrossRef] [Google Scholar]
  147. Stanek, K. Z., Garnavich, P. M., Kaluzny, J., Pych, W., & Thompson, I. 1999, ApJ, 522, L39 [NASA ADS] [CrossRef] [Google Scholar]
  148. Stanek, K. Z., Garnavich, P. M., Jha, S., et al. 2001, ApJ, 563, 592 [NASA ADS] [CrossRef] [Google Scholar]
  149. Steele, I. A., Mundell, C. G., Smith, R. J., Kobayashi, S., & Guidorzi, C. 2009, Nature, 462, 767 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  150. Still, M., Roming, P. W. A., Mason, K. O., et al. 2005, ApJ, 635, 1187 [NASA ADS] [CrossRef] [Google Scholar]
  151. Tang, Q.-W., Peng, F.-K., Wang, X.-Y., & Tam, P.-H. T. 2015, ApJ, 806, 194 [NASA ADS] [CrossRef] [Google Scholar]
  152. Taylor, G. B., Frail, D. A., Berger, E., & Kulkarni, S. R. 2004, ApJ, 609, L1 [NASA ADS] [CrossRef] [Google Scholar]
  153. Taylor, G. B., Momjian, E., Pihlström, Y., Ghosh, T., & Salter, C. 2005, ApJ, 622, 986 [NASA ADS] [CrossRef] [Google Scholar]
  154. Thöne, C. C., Kann, D. A., Jóhannesson, G., et al. 2010, A&A, 523, A70 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  155. Toma, K., Ioka, K., Yamazaki, R., & Nakamura, T. 2006, ApJ, 640, L139 [NASA ADS] [CrossRef] [Google Scholar]
  156. Trotter, A., Frank, N., Lacluyze, A., et al. 2013a, GRB Coordinates Network, 14375 [Google Scholar]
  157. Trotter, A., Frank, N., Lacluyze, A., et al. 2013b, GRB Coordinates Network, 14427 [Google Scholar]
  158. Trotter, A., Reichart, D., Haislip, J., et al. 2013c, GRB Coordinates Network, 15491 [Google Scholar]
  159. Trotter, A., Lacluyze, A., Haislip, J., et al. 2014, GRB Coordinates Network, 16304 [Google Scholar]
  160. Trotter, A., Reichart, D., Haislip, J., et al. 2015, GRB Coordinates Network, 18472 [Google Scholar]
  161. Urata, Y., Tashiro, M. S., Tamagawa, T., et al. 2011, PASJ, 63, 137 [NASA ADS] [CrossRef] [Google Scholar]
  162. Veres, P., Corsi, A., Frail, D. A., Cenko, S. B., & Perley, D. A. 2015, ApJ, 810, 31 [NASA ADS] [CrossRef] [Google Scholar]
  163. Vestrand, W. T., Borozdin, K. N., Brumby, S. P., et al. 2002, in Advanced Global Communications Technologies for Astronomy II, ed. R. I. Kibrick, Proc. SPIE, 4845, 126 [NASA ADS] [CrossRef] [Google Scholar]
  164. Virgili, F. J., Mundell, C. G., & Melandri, A. 2013, GRB Coordinates Network, 14785 [Google Scholar]
  165. Volnova, A., Pozanenko, A., Mundrzyjewski, W., & Tereshenko, V. 2014, GRB Coordinates Network, 16809 [Google Scholar]
  166. Waxman, E. 1997, ApJ, 491, L19 [NASA ADS] [CrossRef] [Google Scholar]
  167. Williams, G. G., Blake, C., & Hartmann, D. 2002, GRB Coordinates Network, 1492 [Google Scholar]
  168. Wren, J., Vestrand, W. T., Wozniak, P., & Davis, H. 2012, GRB Coordinates Network, 14013 [Google Scholar]
  169. Wu, Q., Zou, Y.-C., Cao, X., Wang, D.-X., & Chen, L. 2011, ApJ, 740, L21 [NASA ADS] [CrossRef] [Google Scholar]
  170. Yonetoku, D., Murakami, T., Nakamura, T., et al. 2004, ApJ, 609, 935 [NASA ADS] [CrossRef] [Google Scholar]
  171. Yost, S. A., Alatalo, K., Rykoff, E. S., et al. 2006, ApJ, 636, 959 [NASA ADS] [CrossRef] [Google Scholar]
  172. Yost, S. A., Swan, H. F., Rykoff, E. S., et al. 2007, ApJ, 657, 925 [NASA ADS] [CrossRef] [Google Scholar]
  173. Zaninoni, E., Bernardini, M. G., Margutti, R., Oates, S., & Chincarini, G. 2013, A&A, 557, A12 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  174. Zerbi, R. M., Chincarini, G., Ghisellini, G., et al. 2001, Astron. Nachr., 322, 275 [NASA ADS] [CrossRef] [Google Scholar]
  175. Zhang, B.-B., van Eerten, H., Burrows, D. N., et al. 2015, ApJ, 806, 15 [NASA ADS] [CrossRef] [Google Scholar]
  176. Zhang, Q., Huang, Y. F., & Zong, H. S. 2016, ApJ, 823, 156 [NASA ADS] [CrossRef] [Google Scholar]
  177. Zhao, X.-H., Li, Z., & Bai, J.-M. 2011, ApJ, 726, 89 [NASA ADS] [CrossRef] [Google Scholar]
  178. Zheng, W., & Filippenko, A. V. 2015, GRB Coordinates Network, 17585 [Google Scholar]
  179. Zheng, W., Flewelling, H., Guver, T., & Yuan, F. 2013, GRB Coordinates Network, 14208 [Google Scholar]
  180. Zheng, W., Filippenko, A. V., Morgan, A., & Cenko, S. B. 2014a, GRB Coordinates Network, 16137 [Google Scholar]
  181. Zheng, W., Filippenko, A. V., Morgan, A., & Cenko, S. B. 2014b, GRB Coordinates Network, 16156 [Google Scholar]
  182. Zou, Y.-C., & Piran, T. 2010, MNRAS, 402, 1854 [NASA ADS] [CrossRef] [Google Scholar]
  183. Zou, Y.-C., Fan, Y.-Z., & Piran, T. 2011, ApJ, 726, L2 [NASA ADS] [CrossRef] [Google Scholar]

Appendix A: Additional table

Table A.1

Sample of GRBs with measured tp (69 events) from the peak of the light curve (those from the LAT light curve are labelled “L”).

All Tables

Table 1

Correlation between rest frame tp/ (1 + z) and prompt emission properties (Fig. 4).

In the text
Table 2

Comparison between the different methods of estimating Γ0 found in literature.

In the text
Table 3

Average values and 68% confidence intervals on Γ0 (derived with only measured tp – red line in Fig. 6) or including lower limits (derived from upper limits – black line in Fig. 6).

In the text
Table 4

Correlations between Liso, Eiso, and Ep with Γ0.

In the text
Table 5

Average values and width of the distribution of the (log values of) Eiso, Liso, and Ep, in the rest frame and in the comoving frame (both for the homogeneous and the wind density profile).

In the text
Table A.1

Sample of GRBs with measured tp (69 events) from the peak of the light curve (those from the LAT light curve are labelled “L”).

In the text

All Figures

thumbnail Fig. 1

Cumulative distribution of the afterglow onset time tp (red solid line) in the observer frame for the 66 long GRBs of the “Gold+Silver” sample. The black dashed line (marked with leftward arrows) is the cumulative distribution of 85 upper limits on tp (filtered from Table A.1 according to s). The black solid line (shaded yellow region) is the reconstructed tp distribution (95% confidence interval) accounting for through the Kaplan Meier estimator. The dotted and dot–dashed lines are the cumulative distributions of T90 and Tp,γ (i.e. the time of the peak of the prompt emission light curve), respectively, for the 151 long GRBs. The distributions have been normalised to the respective sample size. Insert: Distribution of tp (solid blue line) and of upper limits (dashed cyan line with leftward arrows) of the 50 GRBs of our sample (16 with tp and 34 with ) also present in the Swift BAT complete sample (Salvaterra et al. 2012).

In the text
thumbnail Fig. 2

Cumulative distribution of the afterglow onset time tp in the rest frame. Same symbols and colour code as in Fig. 1.

In the text
thumbnail Fig. 3

Top panel: GRB duration T90 vs. afterglow peak time tp. GRBs with measured tp are shown with red circles (green stars for LAT bursts). Upper limits on tp are shown by the black arrows (green arrows for LAT GRBs). Bottom panel: time of the peak of the prompt emission light curve Tp,γ vs. tp. Same symbols and colours as in the top panel. In both panels the equality is shown by the dashed line and the short GRB 090510 is shown by the green square.

In the text
thumbnail Fig. 4

Left panel: tp/ (1 + z) vs. Eiso. GRBs with estimates of tp are shown with filled red circles (green symbols for LAT events). Upper limits on tp (Table A.1) are shown by the black arrows (green arrows for LAT events). Middle panel: tp/ (1 + z) vs. Liso. Right panel: tp/ (1 + z) vs. Ep. In all panels the short GRB 090510 is shown by the green square symbol.

In the text
thumbnail Fig. 5

Injection frequency at the tp for each GRB. The optical-R frequency is show by the horizontal line. The injection frequency is shown for the homogeneous and wind case with red and blue symbols. GRBs with tp from the LAT light curve are shown with star symbols. The scaling t− 3/2 of the injection frequency is shown for reference (it is not a fit) by the dashed line. Vertical bars, shown only for the red symbols, represent the position of the injection frequency obtained assuming ϵB in the range 10-4−10-1.

In the text
thumbnail Fig. 6

Cumulative distribution of Γ0 for GRBs with measured tp (red solid line). The distribution of lower limits , derived for GRBs with an upper limit on the onset time , is shown by the dashed black curve (with rightward arrows). Assuming tp ≥ Tp,γ the distribution of upper limits on Γ0 is shown by the dotted grey line (with leftward arrows). The most stringent limit on the distribution of Γ0 is shown by the green solid line which assumes that deceleration radius Rdec ≥ R(τ = 1), that is, the transparency radius (Eq. (16)). Joining estimates of Γ0 and lower limits the reconstructed (through the KM estimator) distribution of Γ0 is shown by the solid black line (and its 95% uncertainty by the yellow shaded region). The distributions are normalised to their respective number of elements. The two panels (right and left, respectively) show the case of a homogeneous (s = 0) and wind (s = 2) medium.

In the text
thumbnail Fig. 7

Correlation between the isotropic equivalent luminosity Liso and the bulk Lorentz factor Γ0. Estimates of Γ0 from the measured afterglow onset tp for 68 long GRBs (red filled circles and green filled stars) and the short GRB 090510 (green filled square symbol). Lower limits (derived from upper limits on the onset time ) are shown by the rightward black arrows. Upper limits (derived assuming that the afterglow onset time is larger than the peak time of the prompt emission, that is, tp ≥ Tp,γ) are shown by the grey (leftward) arrows. Upper limit on Γ0 imposed by the requirement that the deceleration radius is larger than the transparency radius is shown by the grey shaded triangular region (for two values of the radius R0 where the fireball is formed – see Eq. (16)). The solid red line shows the correlation obtained with Γ0 solely (bisector method), while the black dot–dashed line is the correlation obtained through the Monte Carlo method which accounts for the reconstructed distribution of Γ0. Left and right panels show the case of a homogeneous and wind medium, respectively.

In the text
thumbnail Fig. 8

Correlation between the bulk Lorentz factor Γ0 and the isotropic equivalent energy Eiso. Same symbols as Fig. 7.
In the text
thumbnail Fig. 9

Correlation between the bulk Lorentz factor Γ0 and the peak energy Ep. Same symbols as Fig. 7.

In the text
thumbnail Fig. 10

Distribution of comoving frame properties of GRBs. Left panel: comoving frame isotropic luminosity (back and grey histogram for the homogeneous and wind case, respectively) and of the isotropic energy (red and orange histogram for the homogeneous and wind case, respectively). Right panel: comoving frame peak energy (black and grey histogram for the homogeneous and wind case, respectively).

In the text
thumbnail Fig. 11

Distribution of the mass of the jet (in solar masses) for the homogeneous case (red solid line) and for the wind case (blue solid line) computed assuming a typical jet opening angle of 5 degrees.

In the text

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