Free Access
Issue
A&A
Volume 632, December 2019
Article Number A100
Number of page(s) 18
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/201834959
Published online 10 December 2019

© ESO 2019

1. Introduction

Gamma-ray bursts (GRBs) are the most violent explosions in the Universe, releasing 1048 − 1054 ergs (if considered isotropic) typically within a few seconds in gamma rays; but these explosions have been observed up to a few hours in some instances (Zhang et al. 2014; Greiner et al. 2015). Gamma-ray bursts can be divided into two classes depending on their duration: long (> 2 s) and short (≤2 s) (Kouveliotou et al. 1993), the progenitors of which are thought to be the collapse of massive stars or the merger of two compact objects (Zhang & Mészáros 2004; Kumar & Zhang 2015), respectively. In the final stages of merger or collapse, a highly collimated ejecta is released, which has a typical opening angle θjet = 5°–10° (Racusin et al. 2009; Zhang et al. 2015). An internal dissipation process within the jet is thought to produce prompt gamma-ray emission (Rees & Meszaros 1994; Kobayashi et al. 1997; Daigne & Mochkovitch 1998; Maxham & Zhang 2009; Hu et al. 2014), while a longer lived, multiwavelength afterglow is expected to be produced as the jet ploughs into the circumstellar medium (of constant density or a stellar-wind-like density; Mészáros & Rees 1997; Sari & Piran 1997). The relativistic effect implies that emission from the jet is beamed into a cone of half-opening angle 1/Γ0 (Rhoads 1997; Piran 2004; Granot 2007; Liang et al. 2008; van Eerten & MacFadyen 2013), where Γ0 is the initial Lorentz factor of the jet, typically of a value of several hundred (Piran 1999). The beamed geometry leaves a clear signature on the afterglow light curve, manifesting itself as an achromatic break known as a jet break, occurring simultaneously at all frequencies, days to weeks after the burst (Sari et al. 1999; Rhoads 1999). This jet break, resulting in a steeper decay index, occurs when Γ0 has decreased to 1/θjet. The shallower decay index, observed prior to the jet break, is maintained owing to the observer receiving emission from an increasing proportion of the jet as Γ decreases (Zhang et al. 2006). Once the observer sees the entire jet, the jet break is observed. The geometry and angular size of the jet, directly affects measurements of the GRB energy and event rate. The isotropic energy should therefore be corrected by the collimation correction factor, fb = (1 − cos θjet), which solves the energy budget problem (Bloom et al. 2003; Frail et al. 2001; Friedman & Bloom 2005; Kocevski & Butler 2008; Racusin et al. 2009). Hence, the detection of a jet break in the afterglow light curve is an important diagnosis for constraining the outflow geometry and burst energetics. Although the determination of the jet opening angle from the observed break in the afterglow light curves depends on the model (e.g. assumed jet structure, radiation efficiency, and circumburst matter density profile; Sari et al. 1999; Frail et al. 2001).

Much of our current understanding of GRB jets has been built upon observational data. Generations of facilities, including the Compton Gamma-ray Observatory (CGRO), Beppo-SAX, High Energy Transient Explorer 2 (HETE-2), Konus-WIND, INTErnational Gamma-Ray Astrophysics Laboratory (INTEGRAL; Meegan et al. 1992; Aptekar et al. 1995; Costa et al. 1997; Ricker et al. 2003; Rau et al. 2005) have been used to study such catastrophic events since they were first detected almost half a century ago. In particular, the Neil Gehrels Swift Observatory (henceforth Swift), a multiwavelength observatory, has made great contributions to the understanding of the GRB phenomenon since its launch in 2004 (Gehrels et al. 2004). The Burst Alert Telescope (BAT; Barthelmy et al. 2005) on board Swift triggers during the prompt emission of a GRB and broadcasts the location to the ground stations. The rapid slewing capacity of Swift enables the two onboard narrow-band telescopes, the X-ray telescope (XRT; Burrows et al. 2005), and the Ultraviolet Optical Telescope (UVOT; Roming et al. 2005) to observe the X-ray and optical/UV emission within ∼1 min with observations continuing up to several days after the trigger. X-ray emission has even been observed in one case for several years after the trigger (De Pasquale et al. 2016). One of the most important discoveries by Swift/XRT is the existence of a canonical X-ray light curve with five power law components (Zhang et al. 2006): a steep decay, plateau, normal decay, post jet-break decay, and an X-ray flare (in 50% of cases; Falcone et al. 2007).

In addition to UVOT, early IR/optical follow-up is also possible with ground-based robotic telescopes such as MASTER-net (Lipunov et al. 2010) and BOOTES (Castro-Tirado et al. 1999). The MASTER-net (Mobile Astronomical System of Telescope-Robots) includes eight observatories located in Russian, South Africa, Spain (Canarias), and Argentina: MASTER-Amur, MASTER-Tunka, MASTER-Ural, MASTER-Kislovodsk, MASTER-Tavrida, MASTER-SAAO, MASTER-IAC, and MASTER-OAFA. MASTER-net began operating in full mode in 2010 (Lipunov et al. 2004, 2010; Kornilov et al. 2012; Gorbovskoy et al. 2013). Each MASTER-II telescope contains a twin-tube aperture system with a total field of view of 8 square degrees with a photometer in the Johnson-Cousins system and polarising filters that were manufactured using linear conducting nanostructure technology (Kornilov et al. 2012; Gorbovskoy et al. 2013; Ahn et al. 2005).

The Burst Observer and Optical Transient Exploring System (BOOTES1) has been part of the effort to follow-up GRBs since 1998 (Castro-Tirado et al. 1999, 2012). Each BOOTES station has a Ritchey–Chretien 60 cm aperture fast-slewing telescope, which cover a 10′×10′ field of view and is equipped with clear, Sloan gri, and WFCAM/VISTA Z and Y filters. Each system operates autonomously. Swapping from a pre-planned target list to active observations of GRBs is accomplished by switching the filters, focussing, and pointing the telescope to the event coordinates received from the Gamma-ray Coordinates Network (GCN; Barthelmy et al. 1998). Thanks to the capability to react autonomously and to slew promptly, the robotic telescope has increased optical samples, particularly during the early epoch immediately following a GRB trigger.

In this paper, we present observations of GRB 140629A, a GRB that was observed from ∼40 s after the trigger (Yurkov et al. 2014) as a consequence of the rapid dissemination of alerts from space to ground-based telescopes. This allowed us to obtain rich multiwavelength data from early to late epochs (∼4 d), making this object a good case study for constraining jet properties and host environment. We present multiwavelength observations performed by Swift, Konus-WIND, Spitzer, and various ground-based facilities worldwide as well as results of our modelling of the jet and its properties. The rest of the paper is organised as follows: the observations and data reduction are presented in Sect. 2, the analysis of the afterglow and its host galaxy are given in Sect. 3; and discussions are presented in Sect. 4. The concordance cosmology adopted in our analysis has parameters of H0 = 71 kms−1 Mpc−1, ΩM = 0.3 and ΩΛ = 0.7. Errors are given at 1σ unless otherwise stated.

2. Observations and data reduction

2.1. High-energy observations

The Swift/BAT triggered and located GRB 140629A on June 29, 2014 at 14:17:30 UT (T0) (Lien et al. 2014; Evans et al. 2009). The BAT light curve is multiply-peaked with a duration2 T90 = 42 ± 14.3 s (see Fig. 1) and exhibited a peak count rate of ∼2000 counts s−1 in the 15–350 keV range at ∼0 s after the trigger. The time-averaged spectrum from T0-7.53 to 56.47 s was fitted by a simple power law model with a photon index 1.86 ± 0.11 (Cummings et al. 2014). The prompt emission light curve from BAT is shown in Fig. 1. GRB 140629A triggered the S2 detector of the Konus-WIND GRB spectrometer at 14:17:30:00 UT in waiting mode (Golenetskii et al. 2014). This instrument observed a double-peaked light curve3. A power law with an exponential cut-off is the best fit model to the time integrated spectrum with parameters α = 1.42 ± 0.54 and Ep = 86 ± 17 keV. The spectrum resulted in a fluence of 3.4(±0.5)×10−6 erg cm−2 in the 20–10 000 keV energy range. The isotropic energy release in rest frame is Er, iso = 4.4 × 1052 erg (Golenetskii et al. 2014).

thumbnail Fig. 1.

X-ray and optical light curves for GRB 140629A. Observations from both BAT and XRT are given with blue crosses and black crosses, respectively. The BAT data are normalised to the same energy range as XRT. The optical data are shown with circles. The vertical dash line indicates the end of the prompt emission, given by T90.

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The Swift/XRT began observing the field 94.2 s after the BAT trigger and found a bright, fading uncatalogued X-ray source. An astrometrically corrected X-ray position was reported of RA(J2000) = 16h35m54.52s, Dec(J2000) = + 41 °52′36.8″ with an uncertainty of 1.7″ (90% confidence radius; Evans et al. 2014). The initial XRT spectral analysis resulted in a power law photon index of 1.98 ± 0.10 and a column density of 5.2(+2.2, −2.0)×1020 cm−2 (90% confidence), in excess of the galactic value at 3.5σ (9.3 × 1019 cm−2; Osborne et al. 2014).

2.2. Optical observations

2.2.1. MASTER

Three stations of MASTER-net observed GRB 140629A: MASTER-Amur (in Blagoveschensk), MASTER-Tunka (near Baykal Lake), and MASTER-Kislovodsk (Yurkov et al. 2014; Gorbovskoy et al. 2014). The MASTER II robotic telescope in Blagoveschensk pointed to GRB 140629A 33 s after the BAT trigger time (T0) and 15 s after notice time at 14:18:03.19 UT, June 29 (Yurkov et al. 2014) and was the first ground-based telescope to observe the burst. The first two MASTER observations were obtained during the gamma-ray emission. A transient object of brightness 14.26 ± 0.06 mag was detected. Unfortunately, observations at Blagoveschensk were carried out in only one of the two tubes of the twin-tube aperture system as a result of technical disrepair. Observations at this location lasted until ∼800 s after the trigger and finished when weather conditions became unsuitable. During this time ten images with increasing exposure from 10 s to 120 s were obtained.

The MASTER II robotic telescope in Tunka pointed to GRB 140629A 78 s after T0 on June 29, 2014,14:18:48.10 UT, during the evening twilight sky (the Sun was about five degrees below the horizon). For this reason, the first few images are overexposed. Nevertheless, the object is visible at the 4σ level in one polarisation at 14:36:16 UT (1060 s after trigger) with 3 min exposure during the evening sky observations. Following this, a small pause in observations was made for focussing. The observations were restarted in Tunka at 15:01:25 in the R and V filters. From 15:31:52 (∼2600 s after trigger) observations were performed with two mutually perpendicular polarisers. Observations continued until dawn at 18:51:36 UT (∼4.5 h after trigger).

MASTER II in Kislovodsk pointed to GRB 140629A approximately 3.2 h after T0, which was when the weather conditions first became suitable after sunset. A total of about 40 good frames each of 180 s exposure were obtained in white light (C) and R filters. The optical transient is not detected in individual images but is visible in summed images. Frames were grouped into three sets, added together, and processed.

2.2.2. Swift/UVOT

Following the detection by Swift/BAT and XRT, the UVOT began settled observations 101.15 s after the BAT trigger and detected a fading candidate consistent with the XRT error circle (Breeveld & Lien 2014). A series of images was taken with v, b, u, uvw1, uvm2, uvw2, and white filters. The source was detected in all filters, except uvm2 and uvw2.

2.2.3. BOOTES

The 60 cm robotic telescope BOOTES-2/TELMA, in Instituto de Hortofruticultura Subtropical y Mediterránea (IHSM) La Mayora (UMA-CSIC), Málaga, Spain (Castro-Tirado et al. 2012) automatically responded to the GRB alert as soon as its position was observable. Observations started on June 29, 22:19:47.227 UT, ∼8 h after the Swift/BAT trigger, in the clear and Sloan-i band filters, with exposure of 60 s. The source was observed until 2014-06-30 03:46:32.804 UT, ∼13.5 h after the burst. The clear exposures were smeared and were thus discarded. For the i-band exposures the object was faint and not visible in the single frames, but it was detectable in stacked images.

2.2.4. OSN

At the Sierra Nevada mountain range (Granada, Spain), the 1.5 m telescope of Sierra Nevada Observatory (OSN)4 pointed to the source at 2014-06-29 21:06:27.23 UT ∼6.82 h after trigger. The GRB field was also observed on June 30 and July 3. A series of images were obtained in Johnson–Cousins broadband filters: R filter with 300 s exposure and V, I filters with 600 s exposure.

2.2.5. BTA

The optical counterpart of GRB 140629A was also observed with the 6 m Big Telescope Alt-azimuth (BTA) of SAO-RAS (Caucasus Mountains, Russia) on June 29, starting 4.1 h after the detection of the burst by Swift (Lien et al. 2014; Yurkov et al. 2014). Observations of the field were carried out with the Scorpio-I optical reducer (Afanasiev & Moiseev 2005) set in the BTA primary focus. Long-slit spectroscopy was also taken with the grism VPHG440, covering 4000–9800 Å. A 43.6 min spectral observation was obtained. The particular configuration of the device in combination with the 1″ slit achieves a resolution of full width at half maximum (FWHM) = 13 Å.

2.2.6. GTC

The 10.4 m Gran Telescopio CANARIAS5 (GTC, Canary Islands, Spain) obtained several images with the Optical System for Imaging and low Resolution Integrated Spectroscopy (OSIRIS) camera (Cepa et al. 2000) on Feburary 27, 2015 and February 7, 2017, ∼8 months and 2.7 yr after the burst respectively in order to detect the host galaxy. Eight images were obtained in the first epoch with Sloan-g, r, i filters. Four images were taken with the Sloan-i filters of 90 s exposure, three images were taken with the Sloan-g filter of 140 s exposure, and one 90 s exposure was taken with the Sloan-r filter. In the second epoch, 22 images were obtained: seven images each in the Sloan-g and Sloan-r filters with 150 s exposure and 120 s exposure, respectively, and eight images with 90 s exposure in Sloan-i band.

2.3. Infrared observations

The Spitzer Space Telescope (SST) also observed the source with the InfraRed Array Camera (IRAC) instrument at a wavelength of 3.6 μm (Perley et al. 2016a,b). The total exposure time is 2 h. The data were downloaded from the Spitzer data archive center6. The source was observed on June 6, 2015, ∼1 yr after the trigger.

3. Data analysis and results

3.1. Photometry

The final photometry for the MASTER telescopes was extracted using the IRAF7 package (Tody 1993). The MASTER observations were taken with the polariser R, V, and C bands (P0, P45, P90, V, R, C at Fig. 1). The C filter is white light corresponding to 0.2B+0.8R. The polarisation observations were taken with orientations 0°, 45°, and 90° to the celestial equator. Automatic astrometric and photometric calibrations were performed with a method common to all MASTER observatories (Kornilov et al. 2012; Gorbovskoy et al. 2013). For these data, a robust ‘centroid’ algorithm was used to determine the background level. This algorithm allows us to exclude the influence of nearby objects. The data were corrected for the fluctuations with atmospheric opacity using the Astrokit programme (Burdanov et al. 2014), which implements a slightly modified algorithm to that described in Everett & Howell (2001). This programme conducts differential photometry using an ensemble of stars that are close to an object. The details of the photometry calibration can be found in Gorbovskoy et al. (2012). For the polarisation observations, stars with zero polarisation are required for the channel calibration. We assume the polarisation of light from stars in the field of view is small. This can be checked using Serkowski law (Serkowski et al. 1975). The difference in magnitudes between two polariser orientations averaged for all reference stars gives the correction that takes into account different channel responses.

Swift/UVOT sky images were downloaded from the Swift science data centre8 and the magnitudes were extracted following standard UVOT procedure (Poole et al. 2008). In this work, a 3σ upper limit is given when signal to noise (S/N) is < 3. For individual filters after 2000 s, the data are binned with (δt)/t = 0.2 to improve the S/N.

In order to obtain the instrumental magnitudes for the other instruments, point spread function (PSF) photometry was applied with the DAOPHOT tool in the IRAF package. Photometric magnitudes from the OSN were calibrated with the nearby reference stars in USNO-B1, GSC2.3 catalogue (Monet et al. 2003; Lasker et al. 2008). For the GTC, magnitudes were calibrated with the standard star STD_PG1323-086D.

The observation log of GRB 140629A is given in Table 1 and the photometry for all filters and polarisations is presented in Table A.1. All magnitudes are presented in Vega system except the GTC host galaxy observations, which are calibrated using the AB system. The final magnitude errors include the systematic error from the reference star calibration. The magnitudes in the table are not corrected for galactic extinction owing to the reddening of E(B − V) = 0.01 in the direction of the burst (Schlegel et al. 1998). For clarity, the afterglow light curves are shown in Fig. 1.

Table 1.

Observation log of GRB 140629A.

3.2. Temporal properties of the afterglow: An empirical fit

In this section, we fitted the light curves in X-ray and optical band with the empirical multi-segment smooth broken power law models (Beuermann et al. 1999; Jóhannesson et al. 2006; Molinari et al. 2007).

The X-ray light curve (0.3–10 keV) was obtained from the UK Swift Science Data Centre at the University of Leicester (Evans et al. 2009). As shown in Fig. 2, the GRB 140629A X-ray light curve appears to show a canonical structure. An initial shallow decay is followed by a normal decay and then a steep decay (Zhang et al. 2006). To ensure two breaks are required, we first attempted to fit the light curve with a single broken power law. This resulted in a poor fit with a reduced chi square (χ2) = 1.32 (110 d.o.f.), and corresponding null hypothesis probability of only 0.01%. We then tested a smooth broken power law, which showed an improvement giving a reduced χ2 = 1.10 (110 d.o.f.). For this model the best fitting parameters are α1 = 0.84 ± 0.02, α2 = 1.87 ± 0.08 with a break time (8.8 ± 1.3)×103 s. We then tried a smooth double broken power law model. This again improved the fit giving a reduced χ2/d.o.f. = 0.99/108. According to the Akaike information criterion (Akaike 1974; Liddle 2007, AIC), the smooth double broken power law model gives a lower AIC value in comparison to the smooth broken power law, suggesting the second break is required. This is also confirmed by the F-test, which suggests the second break is statistically required at more than 3σ confidence. We can also check the need for an additional break using a Monte Carlo simulation. We create 10 000 synthetic light curves by randomly selecting, for each data point of the observed light curve, a new flux and flux error using a Gaussian function for which the mean and standard deviation is equal to the original observed flux and flux error. Each of the synthetic light curves are then fitted with both a broken power law and a double broken power law. From the resulting distribution of the change in reduced χ2, we find that 98.2% of the simulated light curves have a change in reduced χ2 that is equal to or greater than that obtained for the observed X-ray light curve. The Monte Carlo simulation thus suggests that the double break power law is preferred over the broken power law model at the 2σ confidence level. We do not identify any X-ray flares in the light curve.

thumbnail Fig. 2.

Fitting of the X-ray and optical GRB 140629A data. The normalised optical data are shown on the top panel with blue circles. The X-ray data are plotted on the bottom panel with red crosses. The black lines indicate the fitting results.

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For the optical data, we normalised the observations in the different filters to Johnson-R band using the period between 3000 s and 30 000 s, during which the light curves appear to decay in the same fashion (Oates et al. 2007). The resulting light curve is shown in Fig. 2. We exclude data prior to 70 s from our analysis since the data are likely contaminated by the prompt emission. When fitting the optical data, we tested both a smooth double broken and a smooth triple broken power law against the data. The reduced χ2 changed from 1.53 (d.o.f. = 123) to 1.38 (d.o.f. = 121) with the addition of third break. The smooth triple broken power law model is preferred according to F-test, which provides a chance probability of 0.0008. We also used the Monte Carlo method, which we used to determine the significance of improvement of an additional break in the X-ray light curve, on the optical data. The synthetic light curves in optical are fitted with both a double broken and a triple broken power law model. In the resulting distribution of the change in reduced χ2, we find that 99.9% of the simulated light curves have a change in reduced χ2 that is equal or greater than that obtained from the observed light curve. Thus the Monte Carlo simulation suggests the triple break power law is preferred over the double break power law at 3σ confidence level. The values of the best fitting parameters for the optical and X-ray light curves are shown in Table 2 and the temporal fits are shown in Fig. 2.

Table 2.

Results of the best fit model to the X-ray and optical afterglows of GRB 140629A.

3.3. Spectral analysis

3.3.1. Optical spectroscopy

The optical spectrum observed by the 6 m BTA telescope, given in Fig. 3, shows multiple absorption lines that we identify as Lyman-α (Ly-α) absorption, Al III (1854.72 Å, 1862.78 Å), C IV (1548.20 Å, 1550.77 Å), C II (1334.53 Å), N V (1238.81 Å, 1242.80 Å) Fe II (1608.45 Å), Mg II (2803.53 Å, 2796.35 Å), Si II (1260.42 Å, 1304 Å, 1526.72 Å), Si IV (1393.76 Å, 1402.77 Å), and Al II (1670.79 Å). All these absorption features can be attributed to a single intergalactic cloud at a common redshift z = 2.276 ± 0.001. This measurement is consistent with and refines previous determinations (Moskvitin et al. 2014; D’Avanzo et al. 2014; Xin et al. 2018) of the redshift of the GRB and its host galaxy. Both random and systematic errors are included in the uncertainty of the redshift. There are a few absorption features probably due to intervening systems in the line of sight, but we are not able to identify nor determine their redshift. In addition, we do not find any obvious strong emission lines in our spectrum. The absorption lines associated with the host galaxy at z = 2.276 are identified on the spectrum provided in Fig 3.

thumbnail Fig. 3.

Overall view of the optical spectrum from BTA, obtained ∼4.1 h after the GRB 140629A trigger. These metal lines of the absorption system in the GRB host galaxy are labelled in red, showing the corresponding transitions. The wavelength range with strong telluric absorption features are indicated by grey vertical lines.

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We measured the equivalent widths (EW) of the detected absorption features (see Table 3). We found that the C IV line has a rest-frame EW value of 4.11 Å. This makes it the strongest absorption feature in the spectrum, confirming the identifications made by de Ugarte Postigo et al. (2012) and Xin et al. (2018). The EWs of the high ionisation species are higher than average, as compared to the results of de Ugarte Postigo et al. (2012), while the low ionisation species show no peculiarities. This implies the line of sight has a stronger ionisation absorption than is typically found for GRBs. We are also able to derive the EW ratio of C IV/C II = 2.72 ± 0.15, which is consistent with the result of Xin et al. (2018), but higher than the median value found for GRBs in de Ugarte Postigo et al. (2012). We find the ratio of Al III/Al II = 1.44 ± 0.09 and the ratio of Si IV/Si II = 5.1, which are both higher than the median values found for GRBs de Ugarte Postigo et al. (2012). Using the ratios of C IV/C II and Si IV/Si II, we find this GRB to be consistent with the highly ionised tail of the distribution of ionisation ratios of carbon and silicon; see Fig. 11 in de Ugarte Postigo et al. (2012).

Table 3.

Spectroscopic information for the GRB 140629A.

3.3.2. Afterglow spectral analysis

As reported above, our temporal analysis of the X-ray light curve shows it to be best fit by a double broken power law that has two breaks at ∼3000 s and ∼30 000 s. For each X-ray segment, we extracted an X-ray spectrum from the Swift/XRT GRB spectrum repository9 (Evans et al. 2009). We fit each spectra with a power law and two photoelectric absorption components: one for our Galaxy and the other for the host galaxy of the burst. The fitting results are shown in Table 4. The spectral indices of the three spectra are consistent with each other at 1σ confidence. Therefore, the spectral slope does not show any evidence for evolution across the three X-ray light curve segments.

Table 4.

Spectral analysis of the X-ray light curve of the GRB 140629A afterglow fitted with three segments.

In order to constrain the spectral properties of the optical and the X-ray afterglow, we produced spectral energy distributions (SEDs) at ∼775 s and ∼9350 s after trigger, respectively. The joint SEDs (see Fig. 4) were fit using Xspec 12.9.0 in the HEAsoft package (Arnaud 1996). We fit both a power law and a broken power law model to each of the SEDs, including components for dust and photoelectric absorption for both our Galaxy and the GRB host galaxy. We expect the synchrotron cooling frequency to be the cause of the spectral break in the broken power law model, therefore we fixed the difference in the two spectral indices to be Δβ = 0.5. For the extinction in our Galaxy, we fixed the dust component to have an EB − V = 0.0067 (Schlegel et al. 1998) and used the Milky Way (MW) extinction curve. We fixed the hydrogen column density of the MW to be 9.3 × 1019 cm−2 (Kalberla et al. 2005; Osborne et al. 2014). The fitting results are listed in Table 5. We tested three extinction laws for the GRB host galaxy: RV = 3.08, RV = 2.93 and RV = 3.16 for the MW, Small Magellanic Cloud (SMC), and Large Magellanic Cloud (LMC), respectively.

thumbnail Fig. 4.

Optical and X-ray SED in time interval of 9350 s fitted with the SMC×BKP model.

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Table 5.

Fit results for the GRB 140629A afterglow SEDs.

In the SED obtained at 775 s, we consider the power law to be the best model, since the F-test indicates that the broken power law model does not provide a significant improvement. Of the extinction models, the MW model provides the best chi-square of the three extinction models, but the reduced χ2 is similar for all three scenarios. For the SED at 9350 s (see Fig. 4), we find that the SMC model gives a better fit compared to the other two extinction models for both the power law and broken power law models. Again for this SED, the F-test indicates that the broken power law model does not improve the fit compared to the single power law model. The AIC supports this conclusion, as the AIC value increases for the broken power law model compared to the power law model. While there is no strong preference for a particular extinction law for the 775 s, the 9350 s SED clearly indicates the SMC extinction law is the best model. This is consistent with the preference for an SMC extinction law found for a large number of GRBs (Schady et al. 2010). We therefore assume this model during further investigation of this GRB. Comparing the SMC power law models of both SEDs, we find the parameters from the 775 s and 9350 s SEDs are consistent at 3σ confidence level and that the NH values are consistent at 1σ confidence with the best fit values determined from the X-ray spectrum.

3.4. Host galaxy SED fitting

In order to study the GRB host galaxy, late time observations were taken by 10.4 m GTC at two separate epochs. An object was found within the XRT and UVOT error circles in the second epoch, 2.7 yr after the trigger (as shown in Fig. 5). Spitzer also observed the field ∼1 yr after the burst in the infrared in the 3.6 μm band. By this time, the afterglow contribution is negligible.

thumbnail Fig. 5.

Sloan gri-bands false colour image of the field of GRB 140629A taken with the 10.4 m GTC on July 2, 2017. Circle A (yellow dash circle) represents the 4 arcsec radius error circle of the XRT. The circle B (green circle) and C (pink circle) represent the UVOT observation in 0.74 arcsec and 0.42 arcsec, respectively (Lien et al. 2014; Breeveld & Lien 2014). The host galaxy is clearly found at the burst location which looks interacting with the nearby galaxy. North is up and east to the left.

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The brightness of the host galaxy is 24.94 ± 0.24 mag in Sloan-r band, which is within the brightness distribution for GRB host galaxies (see Fig. 2 in Guziy et al. 2005). It is slightly fainter than the reference galaxy at the same distance, where is the r-band absolute magnitude, considering (Lin et al. 1996) and adopting an Einstein-de Sitter Universe model where the spectrum of the galaxy was assumed to be a power law with an index of 2.

The four photometric magnitudes for the host galaxy were fit with a set of galaxy templates based on the models from Bruzual & Charlot (2003) at a fixed redshift (Castro-Tirado et al. 2007; Krühler et al. 2011) using the LePhare package (v.2.2; Arnouts et al. 1999; Ilbert et al. 2006). As shown in Fig. 6, the optical SED is reproduced best (reduced χ2/d.o.f. = 0.1/3) by a template of a galaxy with a starburst age of Gyr and a mass of log(, which is lower but consistent within errors with 109.3M, i.e. the average value of GRB hosts (Savaglio et al. 2009). The absolute bolometric magnitude of the host galaxy is -22.49 mag and the star formation rate (SFR) is yr−1, which is determined from the UV luminosity of the rest-frame SED (Kennicutt 1998). The specific star formation rate (SSFR) for this burst is yr−1.

thumbnail Fig. 6.

GRB 140629A host galaxy observations overlaid with the best fit host galaxy template.

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3.5. Polarisation

According to our observations with the MASTER network, GRB 140629A reached maximum optical brightness ∼150 s after the burst with 13.8 mag in white light, measured using the polariser, after which it decays as a power law. The difference between the signals obtained in the two polarisers for the time interval from 4463 s to 11596 s were computed as . It was found that the dimensionless time-averaged Stokes parameter is Q = 2.5 ± 2.6%. For the derived uncertainty of 2.6%, the 1σ upper limit for the degree of linear polarisation P is about 18% (see Fig. 14 of Gorbovskoy et al. 2012: the value of P = 18% matches 1σ probability L = 100%−68% = 32% for the curve corresponding to a relative accuracy 2.6%). At the same time, a non-evolving, weak polarisation result was obtained by Hiroshima one-shot wide-field polarimeter(HOWPol) at the Kanata telescope. They found P ∼ 2% between ∼70 and ∼1200 s in the burst frame (Fig. 10 in Gorbovskoy et al. 2016). Our upper limit is consistent with their result.

4. Discussion

We have studied the optical and X-ray afterglow of GRB 140629A. There is no strong evidence for spectral evolution, with the spectral indices consistent within 3σ. The optical light curve begins with an initial rise, which decays thereafter with two breaks. The X-ray light curve decays from the start of observations and also decays with two breaks. A weak polarisation signal was found in the afterglow observations and we were able to obtain information on the host by fitting the host galaxy SED. In the following subsections, we examine the closure relations between the temporal and spectral indices. Then we use the data to test the jet structure to determine a plausible scenario to explain this burst. Finally, we explore the properties of the host.

4.1. Closure relationship in optical and X-ray data

The closure relations are a set of equations that relate observational parameters, namely the spectral and temporal indices, with the microphysical parameters, for example p (the electron energy spectral index), υm (the characteristic synchrotron frequency of the electrons at the minimum injection energy) and υc (the cooling frequency). Typically, the closure relations are used to determine the location of the observing bands relative to the synchrotron frequencies, υm and υc, and also the environment in which the burst occurs (Sari et al. 1998; Sari & Piran 1999; Piran 2004; Zhang & Mészáros 2004; Zhang et al. 2006).

For GRB 140629A, we examined the three segments of the X-ray light curve. Spectral evolution is not observed across these segments. We first examined the second segment since this is expected to be consistent with the normal decay phase. After the first break at ∼3000 s, the light curve becomes steeper with . This slope is typical of the normal decay phase (∼1.1–1.5; Zhang et al. 2006). The spectral slope for this segment is . During this phase, the temporal index and spectral index are consistent with the closure relation α = 3β/2, which is for electrons that are slow cooling within the range υm <  υx <  υc without energy injection in a uniform circumstellar medium. The first segment decays with until 3430 s and the spectral index is . We first tested a simple non-injected model and found that neither α = 3β/2, α = (3β + 1)/2 and α = (1 − β)/2 agree with the theoretic prediction (> 3σ). Only the relation α = (3β − 1)/2 in the υx >  υc case can fit the indices at 1σ. However, comparing the best fit closure relations between the first and second segments implies there must be a spectral break between the two segments which is not observed. We therefore examined more complex closure relations that

include energy injection. It is assumed that the luminosity evolves as L(t) = L0(t/tb)q, where q is the luminosity index affected by the energy injection (Zhang et al. 2006). The relation satisfied is α = (q − 1)+(2 + q)β/2. This is for a constant density medium with slow cooling electrons, where υm <  υx <  υc. This relation requires q = 0.59 ± 0.05. This relation can also be used in the case υ <  υm, but we can able to rule this out since the observed slope is 3σ away from the predicted slope. Therefore, the change between the first two segments most likely signals the end of additional energy injection, after which the afterglow enters the normal decay phase. The origin of the shallow decay phase (plateau) is also an issue that has been debated. The plateau may be categorised as having an internal or external origin, depending on the behaviour of the temporal index of the next light curve segment. The internal plateau is followed by a steep decay whose index is larger than 3, even as large as 10. This plateau is a result of the internal dissipation of a millisecond magnetar as it spins down (Liang et al. 2007; Troja et al. 2007; Yi et al. 2014). In this case when the energy injection ceases a sharp drop in the light curve is observed. The decay index following an external origin for the plateau is typically smaller than 3 and is well explained by energy injection into the external shocks from either slower travelling shells that are received later or by a long-lived central engine (Dai & Lu 1998; Zhang & Mészáros 2001; Tang et al. 2019). For GRB 140629A, the plateau is followed by a normal decay with a slope of 1.33, indicating that it has an external origin. The change in slope across the second break at ∼30 000 s is Δα ∼ 1.1. We immediately ruled out several potential interpretations for this break, including the transition of the cooling frequency across the band that predicts a Δα ∼ 0.25 (Sari et al. 1998); an energy injection from refreshed shocks; a long-lasting central engine that predicts a Δα ∼ 0.7 (Rees & Mészáros 1998; Sari & Mészáros 2000; Zhang & Mészáros 2002), or an external density change, which in order to achieve such a large change in alpha, the density would have to decrease by a factor larger than 103 (Nakar & Granot 2007; Fong et al. 2012). Therefore, this observed break can only be explained by the jet geometry, for example a jet break. The light curve after the jet break should follow ∝ tp. For the third segment, the best fitting closure relation is for a spreading jet with slow cooling, where υm <  υx <  υc is consistent with the previous segments. Using the spectral index for X-ray segment 3, we found a temporal slope of −2.82 ± 0.35, which is consistent with the observed temporal slope at 1.1σ.

For the optical afterglow, we excluded data before 70 s from the fitting process as they were observed during the prompt emission phase and thus may be dominated by the tail of the prompt emission. The best fit to the rest of the optical data required four segments. The first segment is an initial rise that peaks at 180 s. This is likely to be the onset of the afterglow and is discussed in more detail in Sect. 4.4. In that section we focus on the decay segments. After the peak, a slope of can be explained by the scenario of energy injection in a slow cooling interstellar medium (ISM) model with υm <  υo <  υc. We obtained a value of q = 0.73 ± 0.04, which is consistent with that derived from the X-ray energy injected decay segment at 2σ confidence level. Furthermore, the next segment with a temporal index of is in agreement with the α = 3β/2 at 3σ confidence level, which also suggests that the afterglow ceases to be energy injected and enters the normal decay phase in which electrons are slow cooling in uniform medium. Other explanations are ruled out because the temporal indices derived using the spectral indices and the other closure relations are inconsistent with the measured values at > 3σ. The last optical decay segment breaks to a steeper decay at a time (∼30 000 s) consistent, at 1σ, with the jet break in the X-ray light curve. The optical decay slope for this segment is shallower than −2.82 ± 0.35 derived using the X-ray spectral index, but is consistent within 3σ confidence level. As the jet break is a geometric effect, it should have an achromatic break time and the same post-break decay index at all frequencies. For GRB 140629A, the break is achromatic in time, but the slopes of the post-break power law components are only marginally consistent; the decay index of the X-ray light curve is steeper than that in the optical. This has also been found for other GRBs such as GRB 050730 and GRB 051109A (Panaitescu 2007).

Overall, our analysis of the optical and X-ray light curves draws a consistent picture. The light curves are both produced by the blast wave jet impinging on the constant density circumstellar medium in the slow cooling regime, where υm <  υo <  υx <  υc. A long-lasting central engine is still active after the prompt emission has vanished, which when it ceases, causes the light curve to enter the normal decay phase. At ∼30 000 s, an achromatic break is observed in the optical and X-ray light curves, which can be attributed to the jet break. The same process in both bands supports the X-ray radiation and the optical radiation originating from a single component outflow.

4.2. Physical model

Following Zhang et al. (2015), we fitted the multiwavelength data with a model based on numerical simulations to obtain further information about the jet. This process is based on a 2D relativistic hydrodynamics (RHD) simulation, which assumes a jet with a top-hat Blandford–McKee profile (Blandford & McKee 1976) that decelerates into a constant density medium. An ISM-type medium can be assumed as it has been found to explain the observations of most GRB afterglows (Panaitescu & Kumar 2001; Racusin et al. 2009; Schulze et al. 2011) and is consistent with our analysis of GRB 140629A, as discussed in the previous section. Other assumptions of the model include that the radiation and dynamics of the collimated relativistic blast wave are assumed to be separate, and that the fraction of energy contained within the magnetic field at the front of the blast wave is low. The RHD simulation is performed with a relativistic adaptive mesh that employs a high-resolution adaptive mesh refinement (AMR) algorithm (Zhang & MacFadyen 2006). This algorithm calculates the radiation transfer at a given observer time, angle, and distance along a line of sight (van Eerten et al. 2012; van Eerten & MacFadyen 2013). The numerical model takes into account all the factors that can affect the shape of a jet break: (i) lateral expansion, (ii) edge effects, and (iii) off-axis effects. By fitting such a model to the optical and X-ray light curves, we are able to constrain some key physical parameters of the jet.

Because the data before the onset of the afterglow are still dominated by the prompt emission, we only model the data after 180 s. We corrected the optical data for extinction from the MW (Schlafly & Finkbeiner 2011; Schlegel et al. 1998) and the host galaxy, assuming the best fit host extinction law from the SED fitting (SMC) (for details see the previous section, Pei 1992; Schady et al. 2007, 2010). We then converted the extinction corrected light curves to flux density at the central wavelength of the corresponding filter. For the X-ray light curve, the galactic and host neutral hydrogen absorption was also corrected to get the intrinsic flux density at 1 keV.

The numerical modelling calculates the flux density at any frequency and observer time. The Markov chain Monte Carlo (MCMC) method is used to determine the best parameter values (i.e. the smallest χ2 value) (Laskar et al. 2016; Sánchez-Ramírez et al. 2017). The parameters determined include the total energy Etot, iso, 53, the fraction of shock energy given to the electrons ϵe, the fraction of shock energy given to the magnetic fields ϵB, the density of the medium n, the electron energy index p, the jet opening angle θjet and the observed angle θobs. The starting ranges for each parameter are θjet∈ [0.045,0.5], Etot, iso, 53∈ [10−10,103], n∈ [10−5,105], p ∈ [2, 4], ϵB∈ [10−10,1], ϵe∈ [10−10,1], and θobs/θjet∈ [0,1]. For more details, see Zhang et al. (2015).

With these settings, the resulting best fit parameters and their uncertainties are listed in Table 6. The uncertainty on the parameters are calculated at the 68% confidence level in the local mode region. The best fit to each light curve for the different wavelengths is shown in Fig. 7 and the parameter distribution is given in Fig. A.1. In this case, the numerical model finds a solution with best fit parameters of θjet ∼ 6.7° and θobs ∼ 3.8°, giving a total energy release of 1.4 × 1054 erg. Since the modelling focusses on the effects of the jet break and based on a 2D RHD simulation, the energy injection is not taken into account, but the fit can still roughly describe most of the data. Furthermore the analytic approach (p = 2.8 ± 0.3) and the simulation both have p values that agree at 3σ. We find the opening angle to be typical of GRBs (Zhang et al. 2015; Racusin et al. 2009). The relative off-axis angle, θobs/θjet = 0.57, is also consistent with the distribution, which peaks at 0.8, given in Zhang et al. (2015). We also obtained a high circumstellar density value that suggests this burst originated in a dense environment. In addition, our value for log ϵB ∼ −5.7 is consistent with the modelling result from (Xin et al. 2018).

Table 6.

Best fit parameters of the numerical simulation to the multiwavelength afterglow.

thumbnail Fig. 7.

Best fit model determined from the numerical simulations overlaid on the observations at different wavelengths. The corresponding frequency is shown on the right corner in each panel in unit of Hz. The x-axis is the time since trigger in units of seconds. The observed flux density of each instrument is indicated on the y-axis in units of mJy. All data were corrected for MW and host galaxy absorption and extinction effects before modelling. Red solid lines represent the modelled light curves.

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4.3. Jet angle and empirical relation

The Er, iso is the energy in gamma-rays calculated assuming that the emission is isotropic. The collimation corrected energy is calculated following

(1)

where fb is the collimation correction factor. For GRB 140629A, from the high-energy emission, we determined the isotropic rest-frame energy to be Eγ,iso = 4.4 × 1052 erg and the observed Epeak = 86 ± 17 keV. The peak of the energy spectrum in the rest frame is Ep, rest = Epeak × (1 + z) = 281 ± 55 keV. The Eγ,iso and Ep, rest of this burst lie within the distribution of Amati correlation as shown in Fig. 8 (Amati et al. 2002, 2008, 2009; Nava et al. 2012).

thumbnail Fig. 8.

Location of GRB 140629A (red point) with the Ghirlanda (black prism) and Amati relation (black circle) derived from other typical GRBs (data from Ghirlanda et al. 2007; Amati et al. 2008, 2009). The two straight lines indicate the two empirical relations.

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From the numerical modelling, we obtained a jet opening angle, θjet = 6.7°. The collimation corrected energy is Eγ = 3.0(±0.3)×1050 erg. Together with Ep, rest, this burst is also consistent with the Ghirlanda relation (Ghirlanda et al. 2004, 2007) also shown in Fig. 8. GRB 140629A is denoted with a red point on both these empirical relations. The bootstrap method is used to estimate their errors. We also tested the relation between Eγ,iso, Ep, rest and tb, rest (the jet break time) known as Liang–Zhang relation (Liang & Zhang 2005), but GRB 140629A is inconsistent with this correlation, shown in Fig. 9. It is unclear why this GRB appears to be an outlier of the Liang–Zhang relation; it could be due to selection effects relating to the GRB prompt emission.

thumbnail Fig. 9.

Location of GRB 140629A (red point) with the Liang–Zhang relation (black circle) derived from other typical GRBs (data from Liang & Zhang 2005). represents the calculated energy with the Liang–Zhang relation. The straight line indicates the empirical relations and the grey zone corresponds to the 3σ confidence level.

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4.4. Early optical rise

Both MASTER and UVOT observed a peak at ∼180 s (see Fig. 1). There are several explanations for this rise based on physical mechanisms and geometric scenarios, which include the passage of the peak synchrotron frequency through the observing band, the reverse shock, decreasing extinction with time, an off-axis jet, two component outflows, and the onset of the forward shock in the case of an isotropic outflow (Oates et al. 2009). We discuss each of these options in turn.

The peak synchrotron frequency of the forward shock νm, f is expected to cross from blue to red frequencies, producing a chromatic peak which evolves at t2/3. If the peak were due to the crossing of νm, f across the optical bands, the spectrum after peak is expected to be consistent with ν−(p − 1)/2 for νm <  ν <  νc (Sari et al. 1998). The effective frequency of the MASTER clear filter and the UVOT white filter are 5746 Å and 3469 Å respectively (Poole et al. 2008; Kornilov et al. 2012), thus the MASTER clear filter is the redder filter. Using the central wavelengths of the white and clear filters converted to frequency and assuming p = 2, we predicted the peak in the bluer filter should appear 54 s earlier than the peak in the redder filter. We therefore selected the optical data from the MASTER clear filter and UVOT white filter between 90 s and 600 s and fit with a smooth broken power law model. The results are shown in Table 7. The measured difference between the peak times of the two filters is only 9 s and are the same within a 1σ confidence level. This is inconsistent with the predicted peak time difference by 4σ. This therefore makes it unlikely that the passage of νm, f is the cause of the peak in the optical filters.

Table 7.

Fitting result from the first optical bump in two filters.

For the reverse shock model, we just considered the constant density medium as this is consistent with the results of Sect. 4.1. In this case, when the peak synchrotron frequency of the reverse shock νm, r is lower than the optical wavelength, νm, r <  νopt, the light curve is expected to decay after the peak with α = (3p + 1)/4 (Zhang et al. 2003) with p ∼ 2 in this case, α ∼ −1.75. On the contrary, if νm, r >  νopt, then the temporal index after the peak should be α ∼ −0.5, which is followed by a decay of α = (3p + 1)/4. The slope after the early optical peak is inconsistent (> 3σ confidence) with both these scenarios for GRB 140629A.

Another option to produce the rise could be dust destruction. An initially high level of dust could cause optical extinction, as this dust is destroyed by the radiation from the GRB, a chromatic peak is produced with different rise indices for the different filters (Klotz et al. 2008). As dust affects the bluest filters more strongly, the redder filters rise less steeply compared to the blue filters. While the redder MASTER filter has a shallower rise compared to the bluer UVOT filter, the slopes are consistent within 1σ and we do not consider this to be a likely cause of the optical rise.

In the forward shock model, a peak is observed when the jet ploughs into the external medium. It is expected to produce an achromatic rise with α ∼ 1 in the thick shell case with a constant density medium (Sari & Piran 1999; Granot et al. 2002). This is consistent with the rising slopes given in Table 7. We can exclude more complex jet geometry such as off-axis viewing and two component outflows. If the observer’s viewing angle is larger than the half-opening angle of the jet a rise is produced when the Lorentz factor Γ decrease to (θobs − θ)−1 (Granot et al. 2002, 2005). However, the modelling result shows that the observer angle is smaller than the half-opening angle, thus this explanation can be excluded. Also the two-component outflow can be ruled out because we find that the afterglow can be explained by a single component outflow in Sect 4.1. Thus, the achromatic peak and consistent slope make the forward shock the most likely option for GRB 140629A early optical bump.

4.5. Initial bulk Lorentz factor

The initial Lorentz factor (Γ0) is an important parameter describing the initial parameters of the jet. A common way to estimate the initial Lorentz factor is to use the peak time of the early afterglow light curve. The peak time determines the deceleration time of the external forward shock and occurs when roughly half of the blast wave jet energy is transferred to the surrounding medium, as predicted in the blast wave jet model (Sari et al. 1999; Kobayashi & Zhang 2007). At this point, the Lorentz factor is half that of the Γ0. For a constant density medium, the initial Lorentz factor can be expressed as

(2)

where z is the redshift, n is the density of the external medium, mp is the proton mass, c is the speed of light, ηγ is the radiation efficiency, and tp is the peak time of the afterglow onset bump (Sari et al. 1999; Liang et al. 2010). In the optical data, we found the early onset bump peaks at ∼180 s after trigger. Using the parameters, n and ηγ, obtained from the modelling, the initial Lorentz factor for GRB 140629A is Γ0 = 118 ± 5. Lü et al. (2012) corrected the coefficient to 1.4 by integration of blastwave dynamics before the deceleration time. Using the revised equation, we obtained a Lorentz factor of Γ0 = 82 ± 4, which is lower than the value of 315 obtained by (Xin et al. 2018). We likely got such different results because the parameters ηγ and n obtained from our modelling are one or two magnitudes higher than those used by Xin et al. (2018). For GRB 140629A, the radiative efficiency, defined as ηγ=Eγ, iso/(Eγ, iso+EK, iso) = Eγ, iso/Etot, iso, is 3.1%; this is within the radiative efficiency distribution for long GRBs (see Fig. 10 in Racusin et al. 2011).

4.6. Properties of the optical polarisation

The optical polarisation of GRBs provides additional clues to determine the structure and radiation mechanisms of the jet (Covino et al. 2004; Granot & Ramirez-Ruiz 2010). Most GRB polarisation observations have been taken during the afterglow as the prompt emission is short lived. Both linear and circular polarisations have been found at optical wavelengths (Covino et al. 1999; Wiersema et al. 2014). In our observation of 140629A, we find an upper limit of P <  18%, which is consistent with the result from HOWPol (Gorbovskoy et al. 2016). Such a low degree of linear polarisation implies this burst is weakly polarised. This is considered to be confirmation that the dominant afterglow emission mechanism is synchrotron radiation. Moreover, the polarisation measurement suggests an average dust-to-gas ratio in the GRB host galaxy along the line of sight lower than our Galaxy (Klose et al. 2004), which is consistent with our findings in Sect. 4.7. It has been proposed that the polarisation light curves have varied trends for various jet structures, especially at the jet break time (Rossi et al. 2004; Granot & Ramirez-Ruiz 2010). However, our polarisation observations were taken before the jet break and therefore we cannot use them to constrain the jet models for this burst.

4.7. Properties of the host galaxy and environment

The optical to X-ray SED at 9350 s gives the NH along the line of sight as 7.2×1021 cm−2, which is higher than that of our Galaxy ( cm−2 ) by two orders of magnitude. In addition, the intrinsic EB − V is 0.083 ± 0.009, which is also one order of magnitude higher than that of our Galaxy. Therefore, the dust-gas ratio along the line of sight to GRB 140629A is NH/AV = 2.96 × 1022 cm−2. This is lower than that of our Galaxy by one order of magnitude and is slightly lower than the mean value of 3.3 × 1022 cm−2 for the SMC extinction model from Schady et al. (2010). This burst does not show any distinct feature in comparison with the other typical GRBs in the NH − AV plane (see Fig. 9 in Littlejohns et al. 2015).

The best fit of the host galaxy SED suggests the host galaxy has a SFR of yr−1. Compared to other GRB host galaxies, the SFR is higher than the median value 2.5 M yr−1 (Savaglio et al. 2009), but within 2σ of the distribution. The host galaxy is consistent with the SFR and stellar mass correlation for star-forming galaxies, known as the star formation main sequence (Daddi et al. 2007), while it is at the edge of the distribution in the GRB sample shown in Fig. 10 (Savaglio et al. 2009). This may indicate the mass of this galaxy is lower than other semi-SFR galaxies, although the errors are fairly large. The SSFR is higher than the average value of 0.8Gyr−1, but it follows the correlation between the SSFRs and the stellar mass (Christensen et al. 2004; Savaglio et al. 2009), as shown in Fig. 11. The growth timescale in this case is lower than the Hubble time (Savaglio et al. 2009) at the burst distance, which suggests the galaxy is in a bursty mode.

thumbnail Fig. 10.

SFR vs. stellar mass for a sample of GRB hosts, inferred from template fitting to their photometric SEDs. The host of GRB 140629A is shown by a green star. Black squares and red dots represent the long burst and short burst hosts with SFRs measured from GHostS from 1997 to 2014 (Savaglio et al. 2006, 2009). The dashed line indicates a constant SSFR of 1 Gyr−1.

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thumbnail Fig. 11.

SSFR vs. stellar mass for a sample of GRB hosts, inferred from template fitting to their photometric SEDs. The host of GRB 140629A is shown by a green star. Black dots indicate burst hosts with SFRs measured from GHostS from 1997 to 2014 (Savaglio et al. 2006, 2009). The dashed lines indicate the constant SFRs of 0.1, 1, 10, and 100 M yr−1 from left to right.

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Damped Lyman-alpha (DLA; Wolfe et al. 1986, 2005) systems trace the bulk of neutral hydrogen available for star formation processes and are usually found in the lines of sight towards quasars (QSOs; Noterdaeme et al. 2012; Sánchez-Ramírez et al. 2016) and GRBs (Fynbo et al. 2009; Thöne et al. 2013). Since GRBs are produced in star-forming regions, their sight-lines probe their surrounding neutral environments within a few hundred parsecs of the sites of the bursts (Vreeswijk et al. 2013; D’Elia et al. 2014). Hence, burst afterglow absorption spectroscopy at z ≥ 1.8 (for which the Lyman-alpha absorption line is red-shifted out of the atmospheric blue cut-off) provides a unique tool to investigate the amount of metals produced by vicinal star formation process. At a redshift of 2.276, GRB 140629A is therefore at a suitable distance from which we were able to obtain the constituents of the GRB environment.

In order to investigate the neutral hydrogen content at the redshift of the host galaxy, we fitted the red damping wing of the Lyman-alpha absorption with the Voigt profile using the same prescription and tools described in Sánchez-Ramírez et al. (2016), obtaining a column density value of log NHI = 21.0 ± 0.3, as shown in Fig. 12. The large error in the fit mostly comes from the uncertainties in the continuum determination due to the low S/N at the blue end of the spectra. In particular, the neutral hydrogen column density is the characteristic indicator with which to distinguish if the host galaxy is a DLA system, by definition of NHI ≥ 2 × 1020 cm−2 (Wolfe et al. 2005). Therefore, the associated absorption system is technically classified as a DLA.

thumbnail Fig. 12.

Voigt profile fit to the DLA in the spectrum of GRB 140629A. The figure shows the data (black solid line) and the best fit damped profile (blue solid line) with its 1σ confidence interval (cyan area).

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The measured column density is lower than the peak value NHI = 1021.5 cm−2 found in the GRB-DLAs distribution (Fynbo et al. 2009), but is still higher than the mean value of QSO-DLAs. Compared to other GRB-DLAs, this one does not show any properties distinct to the sample of bursts in Fig. 4 of Toy et al. (2016). This value is unusual in the QSO-DLA sample, but frequently observed in GRB sight-lines, suggesting once more that both samples are drawn from distinct populations.

In addition, we also identified strong high-ionisation lines, such as C IV, SiIV, and N V, which are present at the redshift of the absorber. In a previous analysis, it was found that the EWs of the GRB absorption feature are, on average, 2.5 times larger than those in QSO-DLAs (de Ugarte Postigo et al. 2012). As shown in Table 3, those features in GRB 140629A are still consistent and even have an excess on the high-ionisation lines, which is at least six times larger than the median value from QSO sample. Therefore, this burst also provides evidence for EWs in GRB-DLA systems being larger than those in QSO-DLAs. That may imply that GRBs are produced inside the most luminous regions of star-forming galaxies and that the light from the burst has to interact with much more host galaxy material. The N V lines can be used to trace collisionally ionised gas near long GRBs, since N3+ has a high ionisation potential that makes the production of N4+ difficult. The cold N V lines indicate that the GRB progenitor occurred within a dense environment n ≥ 103 cm−2 (Prochaska et al. 2008) within the photo-ionisation scenario. This indirectly supports the dense medium found through numerical modelling of GRB 140629A. Nevertheless, we can neither constrain the distance of the N V absorption to the progenitor, nor the metal abundance owing to the low S/N ratio and resolution of the spectrum.

5. Conclusions

Thanks to the rapid response of several robotic telescopes and continued follow up by larger facilities, in this paper we are able, to present multiwavelength photometric and spectroscopic observations of the long duration GRB 140629A, providing a unique dataset on which to test models for this GRB. A detailed analysis of this burst was carried out to uncover the jet and host galaxy properties. This analysis is based on the data obtained by MASTER net, OSN, BOOTES, GTC, and BTA, as well as the public data from Swift and Spitzer. Optical spectroscopy obtained with BTA shows absorption features at a redshift of z = 2.276 ± 0.001 for this burst.

The signals in two orthogonal polarisations, measured by the MASTER telescope of GRB 140629A, set an upper limit of 18% at 1σ confidence level which implies that it is a weakly polarised burst and that synchrotron radiation dominates the afterglow emission. Using the closure relations, we found that the afterglow in X-ray and optical bands can be well explained by a long-lasting central engine which produces continued energy injection at the beginning. After the end of energy injection, the normal decay phase is observed in both bands. The scenario in which a blast wave jet expands in a constant density ISM with slow cooling electrons, in the range υm <  υo <  υx <  υc, can describe this burst well during the phases with and without energy injection. We identify the final X-ray break at 31 000 s as a jet break. This break is achromatic and is observed in the optical at the same time and has break times consistent at 1σ. The afterglow is well explained by a single component outflow.

We also attempted to model the broadband data with a blast wave jet model based on the prescription of Zhang et al. (2015). The modelled result shows that this burst has a total energy release of 1.4 × 1054 erg s with an opening angle of 6.7° viewed 3.8° off-axis. In addition, a high circumstellar density is obtained from modelling and is also inferred indirectly from the identification of a high ionisation line (N V).

After correcting for redshift and the opening angle, for GRB 140629A we find the peak energy in the rest frame and collimation-corrected energy are consistent with the Ghirlanda and Amati relations but not with the Liang–Zhang relation. The optical light curve displays a peak, which we identified as the afterglow onset produced by the forward shock which is the Γ0 indicator. The onset is found at 181 s and indicates an initial Lorentz factor of 82–118.

Based on analysis of the host galaxy photometry, a low mass galaxy template with a SFR of yr−1 at an age of Gyr is obtained. This result implies the host galaxy is consistent with the star formation main sequence in a star-forming galaxy. Fitting the spectroscopy at 4000 Å with a Voigt profile, a neutral hydrogen density log NHI  =  21.0 ± 0.3 derived indicates that we detect a DLA system in the GRB host galaxy.


2

T90 is burst duration defined as the time interval over which 5%–95% of the counts are accumulated.

7

IRAF is distributed by the National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc. under cooperative agreement with the National Science Foundation. http://ast.noao.edu/data/software

Acknowledgments

Acknowledge the support by the program of China Scholarships Council (CSC) under the Grant no. 201406660015. We also acknowledge support from the Spanish MINEICO ministry and European FEDER funds AYA-2015-71718-R. SRO gratefully acknowledges the support of the Leverhulme Trust Early Career Fellowship. RS-R acknowledges support from ASI (Italian Space Agency) through the Contract no. 2015-046-R.0 and from European Union Horizon 2020 Programme under the AHEAD project (grant agreement no. 654215). MASTER equipment is supported by Lomonosov MSU Development Program and by Moscow Union OPTIKA. VL,EG, NT, VK are supported by BRICS RFBR grant 17-52-80133. MASTER-Tunka equipment is supported of Russian Federation Ministry of Science and High Education (grants 2019-05-592-0001-7293 and 2019-05-595-0001-2496). B.-B.Z. acknowledges support from the National Key Research and Development Program of China (2018YFA0404204), and NSFC-11833003. S.B.P. acknowledges BRICS grant DST/IMRCD/BRICS/Pilotcall/ProFCheap/2017(G) for this work. I.D. acknowledges L. Piro his invitation and financial support to visit and work at IAPS (Rome). We also acknowledge the use of the public data from the Swift data archive. We thank the excellent support form the GTC staff which is located at Observatorio del Roque de los Muchachos at Canary Islands (Spain). Thanks to the data support by NASA with Spitzer Space Telescope. SP and RB acknowledge support from RBRF grant 17-52-80139 BRICS-a. IHP acknowledges support from NRF 2018R1A2A1A05022685. Finally, we want to thank the anonymous referee for his/her comments, which have substantially improved the manuscript.

References

  1. Afanasiev, V. L., & Moiseev, A. V. 2005, Astron. Lett., 31, 194 [NASA ADS] [CrossRef] [Google Scholar]
  2. Ahn, S.-W., Lee, K.-D., Kim, J.-S., et al. 2005, Nanotechnology, 16, 1874 [NASA ADS] [CrossRef] [Google Scholar]
  3. Akaike, H. 1974, IEEE Trans. Autom. Control, 19, 716 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  4. Amati, L., Frontera, F., Tavani, M., et al. 2002, A&A, 390, 81 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Amati, L., Guidorzi, C., Frontera, F., et al. 2008, MNRAS, 391, 577 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  6. Amati, L., Frontera, F., & Guidorzi, C. 2009, A&A, 508, 173 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  7. Aptekar, R. L., Frederiks, D. D., Golenetskii, S. V., et al. 1995, Space Sci. Rev., 71, 265 [NASA ADS] [CrossRef] [Google Scholar]
  8. Arnaud, K. A. 1996, ASP Conf. Ser., 101, 17 [Google Scholar]
  9. Arnouts, S., Cristiani, S., Moscardini, L., et al. 1999, MNRAS, 310, 540 [NASA ADS] [CrossRef] [Google Scholar]
  10. Barthelmy, S. D., Butterworth, P., Cline, T. L., & Gehrels, N. 1998, American Astronomical Society Meeting Abstracts, #192, 43.11 [NASA ADS] [Google Scholar]
  11. Barthelmy, S. D., Barbier, L. M., Cummings, J. R., et al. 2005, Space Sci. Rev., 120, 143 [NASA ADS] [CrossRef] [Google Scholar]
  12. Beuermann, K., Hessman, F. V., Reinsch, K., et al. 1999, A&A, 352, L26 [NASA ADS] [Google Scholar]
  13. Blandford, R. D., & McKee, C. F. 1976, Phys. Fluids, 19, 1130 [NASA ADS] [CrossRef] [Google Scholar]
  14. Bloom, J. S., Frail, D. A., & Kulkarni, S. R. 2003, ApJ, 594, 674 [NASA ADS] [CrossRef] [Google Scholar]
  15. Breeveld, A. A., & Lien, A. Y. 2014, GRB Coordinates Network, 16494, 1 [Google Scholar]
  16. Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000 [NASA ADS] [CrossRef] [Google Scholar]
  17. Burdanov, A. Y., Krushinsky, V. V., & Popov, A. A. 2014, Astrophys. Bull., 69, 368 [NASA ADS] [CrossRef] [Google Scholar]
  18. Burrows, D. N., Hill, J. E., Nousek, J. A., et al. 2005, Space Sci. Rev., 120, 165 [NASA ADS] [CrossRef] [Google Scholar]
  19. Castro-Tirado, A. J., Soldán, J., Bernas, M., et al. 1999, A&AS, 138, 583 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  20. Castro-Tirado, A. J., Bremer, M., McBreen, S., et al. 2007, A&A, 475, 101 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  21. Castro-Tirado, A. J., Jelínek, M., Gorosabel, J., et al. 2012, Astron. Soc. India Conf. Ser., 7, 313 [Google Scholar]
  22. Cepa, J., Aguiar, M., Escalera, V. G., et al. 2000, Proc. SPIE, 4008, 623 [NASA ADS] [CrossRef] [Google Scholar]
  23. Christensen, L., Hjorth, J., & Gorosabel, J. 2004, A&A, 425, 913 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Costa, E., Frontera, F., Heise, J., et al. 1997, Nature, 387, 783 [NASA ADS] [CrossRef] [Google Scholar]
  25. Covino, S., Lazzati, D., Ghisellini, G., et al. 1999, A&A, 348, L1 [NASA ADS] [Google Scholar]
  26. Covino, S., Ghisellini, G., Lazzati, D., & Malesani, D. 2004, ASP Conf. Ser., 312, 169 [NASA ADS] [Google Scholar]
  27. Cummings, J. R., Barthelmy, S. D., Baumgartner, W. H., et al. 2014, GRB Coordinates Network, 16481, 1 [Google Scholar]
  28. Daddi, E., Dickinson, M., Morrison, G., et al. 2007, ApJ, 670, 156 [NASA ADS] [CrossRef] [Google Scholar]
  29. Dai, Z. G., & Lu, T. 1998, A&A, 333, L87 [NASA ADS] [Google Scholar]
  30. Daigne, F., & Mochkovitch, R. 1998, MNRAS, 296, 275 [NASA ADS] [CrossRef] [Google Scholar]
  31. D’Avanzo, P., Malesani, D., D’Elia, V., et al. 2014, GRB Coordinates Network, 16493, 1 [Google Scholar]
  32. De Pasquale, M., Page, M. J., Kann, D. A., et al. 2016, MNRAS, 462, 1111 [NASA ADS] [CrossRef] [Google Scholar]
  33. de Ugarte Postigo, A., Fynbo, J. P. U., Thöne, C. C., et al. 2012, A&A, 548, A11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  34. D’Elia, V., Fynbo, J. P. U., Goldoni, P., et al. 2014, A&A, 564, A38 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  35. Evans, P. A., Beardmore, A. P., Page, K. L., et al. 2009, MNRAS, 397, 1177 [NASA ADS] [CrossRef] [Google Scholar]
  36. Evans, P. A., Goad, M. R., Osborne, J. P., & Beardmore, A. P. 2014, GRB Coordinates Network, 16479, 1 [Google Scholar]
  37. Everett, M. E., & Howell, S. B. 2001, PASP, 113, 1428 [NASA ADS] [CrossRef] [Google Scholar]
  38. Falcone, A. D., Morris, D., Racusin, J., et al. 2007, ApJ, 671, 1921 [NASA ADS] [CrossRef] [Google Scholar]
  39. Fong, W., Berger, E., Margutti, R., et al. 2012, ApJ, 756, 189 [NASA ADS] [CrossRef] [Google Scholar]
  40. Frail, D. A., Kulkarni, S. R., Sari, R., et al. 2001, ApJ, 562, L55 [NASA ADS] [CrossRef] [Google Scholar]
  41. Friedman, A. S., & Bloom, J. S. 2005, ApJ, 627, 1 [NASA ADS] [CrossRef] [Google Scholar]
  42. Fynbo, J. P. U., Jakobsson, P., Prochaska, J. X., et al. 2009, ApJS, 185, 526 [NASA ADS] [CrossRef] [Google Scholar]
  43. Gehrels, N., Chincarini, G., Giommi, P., et al. 2004, ApJ, 611, 1005 [NASA ADS] [CrossRef] [Google Scholar]
  44. Ghirlanda, G., Ghisellini, G., & Lazzati, D. 2004, ApJ, 616, 331 [NASA ADS] [CrossRef] [Google Scholar]
  45. Ghirlanda, G., Nava, L., Ghisellini, G., & Firmani, C. 2007, A&A, 466, 127 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Golenetskii, S., Aptekar, R., Frederiks, D., et al. 2014, GRB Coordinates Network, 16495, 1 [Google Scholar]
  47. Gorbovskoy, E. S., Lipunova, G. V., Lipunov, V. M., et al. 2012, MNRAS, 421, 1874 [NASA ADS] [CrossRef] [Google Scholar]
  48. Gorbovskoy, E. S., Lipunov, V. M., Kornilov, V. G., et al. 2013, Astron. Rep., 57, 233 [NASA ADS] [CrossRef] [Google Scholar]
  49. Gorbovskoy, E., Lipunov, V., Pruzhinskaya, M., et al. 2014, GRB Coordinates Network, 16507, 1 [Google Scholar]
  50. Gorbovskoy, E. S., Lipunov, V. M., Buckley, D. A. H., et al. 2016, MNRAS, 455, 3312 [NASA ADS] [CrossRef] [Google Scholar]
  51. Granot, J. 2007, Rev. Mex. Astron. Astrofis., 27, 140 [Google Scholar]
  52. Granot, J., & Ramirez-Ruiz, E. 2010, ArXiv e-prints [arXiv:1012.5101] [Google Scholar]
  53. Granot, J., Panaitescu, A., Kumar, P., & Woosley, S. E. 2002, ApJ, 570, L61 [NASA ADS] [CrossRef] [Google Scholar]
  54. Granot, J., Ramirez-Ruiz, E., & Perna, R. 2005, ApJ, 630, 1003 [NASA ADS] [CrossRef] [Google Scholar]
  55. Greiner, J., Mazzali, P. A., Kann, D. A., et al. 2015, Nature, 523, 189 [NASA ADS] [CrossRef] [Google Scholar]
  56. Guziy, S., Gorosabel, J., Castro-Tirado, A. J., et al. 2005, A&A, 441, 975 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  57. Hu, Y.-D., Liang, E.-W., Xi, S.-Q., et al. 2014, ApJ, 789, 145 [NASA ADS] [CrossRef] [Google Scholar]
  58. Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. Jóhannesson, G., Björnsson, G., & Gudmundsson, E. H. 2006, ApJ, 640, L5 [NASA ADS] [CrossRef] [Google Scholar]
  60. Kalberla, P. M. W., Burton, W. B., Hartmann, D., et al. 2005, A&A, 440, 775 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  61. Kennicutt, Robert C. 1998, ApJ, 498, 541 [NASA ADS] [CrossRef] [Google Scholar]
  62. Klose, S., Greiner, J., Rau, A., et al. 2004, AJ, 128, 1942 [NASA ADS] [CrossRef] [Google Scholar]
  63. Klotz, A., Gendre, B., Stratta, G., et al. 2008, A&A, 483, 847 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  64. Kobayashi, S., & Zhang, B. 2007, ApJ, 655, 973 [NASA ADS] [CrossRef] [Google Scholar]
  65. Kobayashi, S., Piran, T., & Sari, R. 1997, ApJ, 490, 92 [NASA ADS] [CrossRef] [Google Scholar]
  66. Kocevski, D., & Butler, N. 2008, ApJ, 680, 531 [NASA ADS] [CrossRef] [Google Scholar]
  67. Kornilov, V. G., Lipunov, V. M., Gorbovskoy, E. S., et al. 2012, Exp. Astron., 33, 173 [NASA ADS] [CrossRef] [Google Scholar]
  68. Kouveliotou, C., Meegan, C. A., Fishman, G. J., et al. 1993, ApJ, 413, L101 [NASA ADS] [CrossRef] [Google Scholar]
  69. Krühler, T., Greiner, J., Schady, P., et al. 2011, A&A, 534, A108 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  70. Kumar, P., & Zhang, B. 2015, Phys. Rep., 561, 1 [NASA ADS] [CrossRef] [Google Scholar]
  71. Laskar, T., Alexander, K. D., Berger, E., et al. 2016, ApJ, 833, 88 [NASA ADS] [CrossRef] [Google Scholar]
  72. Lasker, B. M., Lattanzi, M. G., McLean, B. J., et al. 2008, AJ, 136, 735 [NASA ADS] [CrossRef] [Google Scholar]
  73. Liang, E., & Zhang, B. 2005, ApJ, 633, 611 [NASA ADS] [CrossRef] [Google Scholar]
  74. Liang, E.-W., Zhang, B.-B., & Zhang, B. 2007, ApJ, 670, 565 [NASA ADS] [CrossRef] [Google Scholar]
  75. Liang, E.-W., Racusin, J. L., Zhang, B., Zhang, B.-B., & Burrows, D. N. 2008, ApJ, 675, 528 [NASA ADS] [CrossRef] [Google Scholar]
  76. Liang, E.-W., Yi, S.-X., Zhang, J., et al. 2010, ApJ, 725, 2209 [NASA ADS] [CrossRef] [Google Scholar]
  77. Liddle, A. R. 2007, MNRAS, 377, L74 [NASA ADS] [Google Scholar]
  78. Lien, A. Y., Barthelmy, S. D., Bernardini, M. G., et al. 2014, GRB Coordinates Network, 16477, 1 [Google Scholar]
  79. Lin, H., Kirshner, R. P., Shectman, S. A., et al. 1996, ApJ, 464, 60 [NASA ADS] [CrossRef] [Google Scholar]
  80. Lipunov, V. M., Krylov, A. V., Kornilov, V. G., et al. 2004, Astron. Nachr., 325, 580 [NASA ADS] [CrossRef] [Google Scholar]
  81. Lipunov, V., Kornilov, V., Gorbovskoy, E., et al. 2010, Adv. Astron., 2010, 349171 [NASA ADS] [CrossRef] [Google Scholar]
  82. Littlejohns, O. M., Butler, N. R., Cucchiara, A., et al. 2015, MNRAS, 449, 2919 [NASA ADS] [CrossRef] [Google Scholar]
  83. Lü, J., Zou, Y.-C., Lei, W.-H., et al. 2012, ApJ, 751, 49 [NASA ADS] [CrossRef] [Google Scholar]
  84. Maxham, A., & Zhang, B. 2009, ApJ, 707, 1623 [NASA ADS] [CrossRef] [Google Scholar]
  85. Meegan, C. A., Fishman, G. J., Wilson, R. B., et al. 1992, Nature, 355, 143 [NASA ADS] [CrossRef] [Google Scholar]
  86. Mészáros, P., & Rees, M. J. 1997, ApJ, 476, 232 [NASA ADS] [CrossRef] [Google Scholar]
  87. Molinari, E., Vergani, S. D., Malesani, D., et al. 2007, A&A, 469, L13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  88. Monet, D. G., Levine, S. E., Canzian, B., et al. 2003, AJ, 125, 984 [NASA ADS] [CrossRef] [Google Scholar]
  89. Moskvitin, A., Burenin, R., Uklein, R., et al. 2014, GRB Coordinates Network, 16489, 1 [Google Scholar]
  90. Nakar, E., & Granot, J. 2007, MNRAS, 380, 1744 [NASA ADS] [CrossRef] [Google Scholar]
  91. Nava, L., Salvaterra, R., Ghirlanda, G., et al. 2012, MNRAS, 421, 1256 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  92. Noterdaeme, P., Laursen, P., Petitjean, P., et al. 2012, A&A, 540, A63 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  93. Oates, S. R., de Pasquale, M., Page, M. J., et al. 2007, MNRAS, 380, 270 [NASA ADS] [CrossRef] [Google Scholar]
  94. Oates, S. R., Page, M. J., Schady, P., et al. 2009, MNRAS, 395, 490 [NASA ADS] [CrossRef] [Google Scholar]
  95. Osborne, J. P., Page, K. L., Pagani, C., et al. 2014, GRB Coordinates Network, 16490, 1 [Google Scholar]
  96. Panaitescu, A. 2007, MNRAS, 380, 374 [NASA ADS] [CrossRef] [Google Scholar]
  97. Panaitescu, A., & Kumar, P. 2001, ApJ, 560, L49 [NASA ADS] [CrossRef] [Google Scholar]
  98. Pei, Y. C. 1992, ApJ, 395, 130 [NASA ADS] [CrossRef] [Google Scholar]
  99. Perley, D. A., Krühler, T., Schulze, S., et al. 2016a, ApJ, 817, 7 [NASA ADS] [CrossRef] [Google Scholar]
  100. Perley, D. A., Tanvir, N. R., Hjorth, J., et al. 2016b, ApJ, 817, 8 [NASA ADS] [CrossRef] [Google Scholar]
  101. Piran, T. 1999, Phys. Rep., 314, 575 [NASA ADS] [CrossRef] [Google Scholar]
  102. Piran, T. 2004, Rev. Mod. Phys., 76, 1143 [NASA ADS] [CrossRef] [Google Scholar]
  103. Poole, T. S., Breeveld, A. A., Page, M. J., et al. 2008, MNRAS, 383, 627 [NASA ADS] [CrossRef] [Google Scholar]
  104. Prochaska, J. X., Dessauges-Zavadsky, M., Ramirez-Ruiz, E., & Chen, H.-W. 2008, ApJ, 685, 344 [NASA ADS] [CrossRef] [Google Scholar]
  105. Racusin, J. L., Liang, E. W., Burrows, D. N., et al. 2009, ApJ, 698, 43 [NASA ADS] [CrossRef] [Google Scholar]
  106. Racusin, J. L., Oates, S. R., Schady, P., et al. 2011, ApJ, 738, 138 [NASA ADS] [CrossRef] [Google Scholar]
  107. Rau, A., Kienlin, A. V., Hurley, K., & Lichti, G. G. 2005, A&A, 438, 1175 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  108. Rees, M. J., & Meszaros, P. 1994, ApJ, 430, L93 [NASA ADS] [CrossRef] [Google Scholar]
  109. Rees, M. J., & Mészáros, P. 1998, ApJ, 496, L1 [NASA ADS] [CrossRef] [Google Scholar]
  110. Rhoads, J. E. 1997, ApJ, 487, L1 [NASA ADS] [CrossRef] [Google Scholar]
  111. Rhoads, J. E. 1999, ApJ, 525, 737 [NASA ADS] [CrossRef] [Google Scholar]
  112. Ricker, G. R., Atteia, J. L., Crew, G. B., et al. 2003, AIP, 662, 3 [NASA ADS] [CrossRef] [Google Scholar]
  113. Roming, P. W. A., Kennedy, T. E., Mason, K. O., et al. 2005, Space Sci. Rev., 120, 95 [NASA ADS] [CrossRef] [Google Scholar]
  114. Rossi, E. M., Lazzati, D., Salmonson, J. D., & Ghisellini, G. 2004, MNRAS, 354, 86 [NASA ADS] [CrossRef] [Google Scholar]
  115. Sánchez-Ramírez, R., Ellison, S. L., Prochaska, J. X., et al. 2016, MNRAS, 456, 4488 [NASA ADS] [CrossRef] [Google Scholar]
  116. Sánchez-Ramírez, R., Hancock, P. J., Jóhannesson, G., et al. 2017, MNRAS, 464, 4624 [NASA ADS] [CrossRef] [Google Scholar]
  117. Sari, R., & Mészáros, P. 2000, ApJ, 535, L33 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  118. Sari, R., & Piran, T. 1997, MNRAS, 287, 110 [NASA ADS] [CrossRef] [Google Scholar]
  119. Sari, R., & Piran, T. 1999, ApJ, 520, 641 [NASA ADS] [CrossRef] [Google Scholar]
  120. Sari, R., Piran, T., & Narayan, R. 1998, ApJ, 497, L17 [NASA ADS] [CrossRef] [Google Scholar]
  121. Sari, R., Piran, T., & Halpern, J. P. 1999, ApJ, 519, L17 [NASA ADS] [CrossRef] [Google Scholar]
  122. Savaglio, S., Glazebrook, K., & Le Borgne, D. 2006, AIP, 836, 540 [NASA ADS] [Google Scholar]
  123. Savaglio, S., Glazebrook, K., & Le Borgne, D. 2009, ApJ, 691, 182 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  124. Schady, P., Mason, K. O., Page, M. J., et al. 2007, MNRAS, 377, 273 [NASA ADS] [CrossRef] [Google Scholar]
  125. Schady, P., Page, M. J., Oates, S. R., et al. 2010, MNRAS, 401, 2773 [NASA ADS] [CrossRef] [Google Scholar]
  126. Schlafly, E. F., & Finkbeiner, D. P. 2011, ApJ, 737, 103 [NASA ADS] [CrossRef] [Google Scholar]
  127. Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525 [NASA ADS] [CrossRef] [Google Scholar]
  128. Schulze, S., Klose, S., Björnsson, G., et al. 2011, A&A, 526, A23 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  129. Serkowski, K., Mathewson, D. S., & Ford, V. L. 1975, ApJ, 196, 261 [NASA ADS] [CrossRef] [Google Scholar]
  130. Tang, C.-H., Huang, Y.-F., Geng, J.-J., & Zhang, Z.-B. 2019, ApJS, 245, 1 [NASA ADS] [CrossRef] [Google Scholar]
  131. Thöne, C. C., Fynbo, J. P. U., Goldoni, P., et al. 2013, MNRAS, 428, 3590 [NASA ADS] [CrossRef] [Google Scholar]
  132. Tody, D. 1993, ASP Conf. Ser., 52, 173 [NASA ADS] [Google Scholar]
  133. Toy, V. L., Cucchiara, A., Veilleux, S., et al. 2016, ApJ, 832, 175 [NASA ADS] [CrossRef] [Google Scholar]
  134. Troja, E., Cusumano, G., O’Brien, P. T., et al. 2007, ApJ, 665, 599 [NASA ADS] [CrossRef] [Google Scholar]
  135. van Eerten, H., & MacFadyen, A. 2013, ApJ, 767, 141 [NASA ADS] [CrossRef] [Google Scholar]
  136. van Eerten, H., van der Horst, A., & MacFadyen, A. 2012, ApJ, 749, 44 [NASA ADS] [CrossRef] [Google Scholar]
  137. Vreeswijk, P. M., Ledoux, C., Raassen, A. J. J., et al. 2013, A&A, 549, A22 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  138. Wiersema, K., Covino, S., Toma, K., et al. 2014, Nature, 509, 201 [NASA ADS] [CrossRef] [Google Scholar]
  139. Wolfe, A. M., Turnshek, D. A., Smith, H. E., & Cohen, R. D. 1986, ApJS, 61, 249 [NASA ADS] [CrossRef] [Google Scholar]
  140. Wolfe, A. M., Gawiser, E., & Prochaska, J. X. 2005, ARA&A, 43, 861 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  141. Xin, L.-P., Zhong, S.-Q., Liang, E.-W., et al. 2018, ApJ, 860, 8 [NASA ADS] [CrossRef] [Google Scholar]
  142. Yi, S. X., Dai, Z. G., Wu, X. F., & Wang, F. Y. 2014, Unpublished [arXiv:1401.1601] [Google Scholar]
  143. Yurkov, V., Sergienko, Y., Varda, D., et al. 2014, GRB Coordinates Network, 16478, 1 [Google Scholar]
  144. Zhang, W., & MacFadyen, A. I. 2006, ApJS, 164, 255 [NASA ADS] [CrossRef] [Google Scholar]
  145. Zhang, B., & Mészáros, P. 2001, ApJ, 552, L35 [NASA ADS] [CrossRef] [Google Scholar]
  146. Zhang, B., & Mészáros, P. 2002, ApJ, 566, 712 [NASA ADS] [CrossRef] [Google Scholar]
  147. Zhang, B., & Mészáros, P. 2004, Int. J. Mod. Phys. A, 19, 2385 [NASA ADS] [CrossRef] [Google Scholar]
  148. Zhang, B., Kobayashi, S., & Mészáros, P. 2003, ApJ, 595, 950 [NASA ADS] [CrossRef] [Google Scholar]
  149. Zhang, B., Fan, Y. Z., Dyks, J., et al. 2006, ApJ, 642, 354 [NASA ADS] [CrossRef] [Google Scholar]
  150. Zhang, B.-B., Zhang, B., Murase, K., Connaughton, V., & Briggs, M. S. 2014, ApJ, 787, 66 [NASA ADS] [CrossRef] [Google Scholar]
  151. Zhang, B.-B., van Eerten, H., Burrows, D. N., et al. 2015, ApJ, 806, 15 [NASA ADS] [CrossRef] [Google Scholar]

Appendix A: Additional material

Table A.1.

Photometric observations at the GRB 140629A field at optical wavelengths.

thumbnail Fig. A.1.

Triangle plot of the MCMC fitting to our simulation-based model. It shows the posterior distribution and the correlation between the parameters.

Open with DEXTER

All Tables

Table 1.

Observation log of GRB 140629A.

Table 2.

Results of the best fit model to the X-ray and optical afterglows of GRB 140629A.

Table 3.

Spectroscopic information for the GRB 140629A.

Table 4.

Spectral analysis of the X-ray light curve of the GRB 140629A afterglow fitted with three segments.

Table 5.

Fit results for the GRB 140629A afterglow SEDs.

Table 6.

Best fit parameters of the numerical simulation to the multiwavelength afterglow.

Table 7.

Fitting result from the first optical bump in two filters.

Table A.1.

Photometric observations at the GRB 140629A field at optical wavelengths.

All Figures

thumbnail Fig. 1.

X-ray and optical light curves for GRB 140629A. Observations from both BAT and XRT are given with blue crosses and black crosses, respectively. The BAT data are normalised to the same energy range as XRT. The optical data are shown with circles. The vertical dash line indicates the end of the prompt emission, given by T90.

Open with DEXTER
In the text
thumbnail Fig. 2.

Fitting of the X-ray and optical GRB 140629A data. The normalised optical data are shown on the top panel with blue circles. The X-ray data are plotted on the bottom panel with red crosses. The black lines indicate the fitting results.

Open with DEXTER
In the text
thumbnail Fig. 3.

Overall view of the optical spectrum from BTA, obtained ∼4.1 h after the GRB 140629A trigger. These metal lines of the absorption system in the GRB host galaxy are labelled in red, showing the corresponding transitions. The wavelength range with strong telluric absorption features are indicated by grey vertical lines.

Open with DEXTER
In the text
thumbnail Fig. 4.

Optical and X-ray SED in time interval of 9350 s fitted with the SMC×BKP model.

Open with DEXTER
In the text
thumbnail Fig. 5.

Sloan gri-bands false colour image of the field of GRB 140629A taken with the 10.4 m GTC on July 2, 2017. Circle A (yellow dash circle) represents the 4 arcsec radius error circle of the XRT. The circle B (green circle) and C (pink circle) represent the UVOT observation in 0.74 arcsec and 0.42 arcsec, respectively (Lien et al. 2014; Breeveld & Lien 2014). The host galaxy is clearly found at the burst location which looks interacting with the nearby galaxy. North is up and east to the left.

Open with DEXTER
In the text
thumbnail Fig. 6.

GRB 140629A host galaxy observations overlaid with the best fit host galaxy template.

Open with DEXTER
In the text
thumbnail Fig. 7.

Best fit model determined from the numerical simulations overlaid on the observations at different wavelengths. The corresponding frequency is shown on the right corner in each panel in unit of Hz. The x-axis is the time since trigger in units of seconds. The observed flux density of each instrument is indicated on the y-axis in units of mJy. All data were corrected for MW and host galaxy absorption and extinction effects before modelling. Red solid lines represent the modelled light curves.

Open with DEXTER
In the text
thumbnail Fig. 8.

Location of GRB 140629A (red point) with the Ghirlanda (black prism) and Amati relation (black circle) derived from other typical GRBs (data from Ghirlanda et al. 2007; Amati et al. 2008, 2009). The two straight lines indicate the two empirical relations.

Open with DEXTER
In the text
thumbnail Fig. 9.

Location of GRB 140629A (red point) with the Liang–Zhang relation (black circle) derived from other typical GRBs (data from Liang & Zhang 2005). represents the calculated energy with the Liang–Zhang relation. The straight line indicates the empirical relations and the grey zone corresponds to the 3σ confidence level.

Open with DEXTER
In the text
thumbnail Fig. 10.

SFR vs. stellar mass for a sample of GRB hosts, inferred from template fitting to their photometric SEDs. The host of GRB 140629A is shown by a green star. Black squares and red dots represent the long burst and short burst hosts with SFRs measured from GHostS from 1997 to 2014 (Savaglio et al. 2006, 2009). The dashed line indicates a constant SSFR of 1 Gyr−1.

Open with DEXTER
In the text
thumbnail Fig. 11.

SSFR vs. stellar mass for a sample of GRB hosts, inferred from template fitting to their photometric SEDs. The host of GRB 140629A is shown by a green star. Black dots indicate burst hosts with SFRs measured from GHostS from 1997 to 2014 (Savaglio et al. 2006, 2009). The dashed lines indicate the constant SFRs of 0.1, 1, 10, and 100 M yr−1 from left to right.

Open with DEXTER
In the text
thumbnail Fig. 12.

Voigt profile fit to the DLA in the spectrum of GRB 140629A. The figure shows the data (black solid line) and the best fit damped profile (blue solid line) with its 1σ confidence interval (cyan area).

Open with DEXTER
In the text
thumbnail Fig. A.1.

Triangle plot of the MCMC fitting to our simulation-based model. It shows the posterior distribution and the correlation between the parameters.

Open with DEXTER
In the text

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