The First FERMI-LAT Gamma-Ray Burst Catalog

TL;DR: The first Fermi-LAT catalog of gamma-ray bursts (GRBs) is presented in this paper. But it is limited to GRBs detected by the Gamma-Ray Burst Monitor (GBM).

Abstract: In three years of observations since the beginning of nominal science operations in August 2008, the Large Area Telescope (LAT) on board the Fermi Gamma Ray Space Telescope has observed high-energy (>20 MeV) \gamma-ray emission from 35 gamma-ray bursts (GRBs). Among these, 28 GRBs have been detected above 100 MeV and 7 GRBs above ~ 20 MeV. The first Fermi-LAT catalog of GRBs is a compilation of these detections and provides a systematic study of high-energy emission from GRBs for the first time. To generate the catalog, we examined 733 GRBs detected by the Gamma-Ray Burst Monitor (GBM) on Fermi and processed each of them using the same analysis sequence. Details of the methodology followed by the LAT collaboration for GRB analysis are provided. We summarize the temporal and spectral properties of the LAT-detected GRBs. We also discuss characteristics of LAT-detected emission such as its delayed onset and longer duration compared to emission detected by the GBM, its power-law temporal decay at late times, and the fact that it is dominated by a power-law spectral component that appears in addition to the usual Band model.

Summary

1. INTRODUCTION

• Prior to the Fermi Gamma-Ray Space Telescope mission, high-energy emission from gamma-ray bursts (GRBs) was observed with the Energetic Gamma-Ray Experiment Telescope covering the energy range from 30 MeV to 30 GeV (Hughes et al.
• In Section 3, the authors give a detailed description of the analysis methods that they applied to detect and localize GRBs with the LAT, as well as the methodology that they followed to characterize their temporal and spectral properties.

2. DATA PREPARATION

• The authors describe the data analyzed in this study and the list of GRB triggers that they searched for LAT detections.
• The LAT event classes underwent many stages of refinement and were released as different versions (or “passes”) of the data.
• Both the Transient and Diffuse classes offer good energy and angular resolutions, along with large effective areas above 100 MeV and reasonable residual background rates.
• The authors use the LLE data only for source detection and duration measurement.
• The authors perform joint GBM-LAT spectral fitting using the LAT Transient-class data, the GBM Time-Tagged Event (TTE) data and the GBM RSP/RSP2 response files.73.

2.1.1. LAT Data

• The authors select Transient class data with reconstructed energies in the 100 MeV–100 GeV range.
• The upper limit (UL) was chosen at 100 GeV since the authors do not expect to detect GRB photons at such high energies due to the opacity of the universe and the limited effective area of the LAT.
• The exact dependence of the LLE PSF on the off-axis angle is not available yet.
• For the maximum-likelihood analyses, the authors use a fixed-radius ROI set at 12◦, a value larger than the 99% containment radius of the Transient LAT PSF evaluated for a 100 MeV event on axis.
• The authors apply a cut to limit the contamination from γ -rays produced by interactions of cosmic rays with the Earth’s upper atmosphere.

2.1.2. GBM Data

• The response of a GBM detector depends on the continuously varying position of the GRB in its FoV, with its effective area decreasing as the angular distance between the detector boresight and the source (θGBM) increases.
• The authors also exclude any detector occulted by other detectors or the spacecraft during any part of the analyzed time interval, as advised in Goldstein et al. (2012).
• With a suitable weighting scheme, as described in Section 3.4.1, these files provide an adequate description of the GRB detector responses.
• Finally, in some cases, bright GRBs trigger an ARR, causing rapid variations of θGBM with time for some of the GBM detectors.
• These variations create further variations in those detector responses and background rates.

2.2. Input GRB List

• The authors use the localizations provided by the GBM, 74 http://www.slac.stanford.edu/exp/glast/wb/prod/pages/sciTools_gtmktime/ gtmktime.htm.
• In order to characterize their detection algorithm, the authors also created a list of “fake” GBM triggers by considering trigger times earlier than the true GBM trigger time by 11466 s (approximately two orbits).
• Since the most common observing mode for the Fermi spacecraft is to rock between the northern and southern orbital hemispheres on alternate orbits, with the exception of ARRs, the burst triggers of the “fake” sample have the desirable property of having very similar background conditions as those of the true sample.

3. ANALYSIS METHODS AND PROCEDURE

• The sequence consists of event-counting analyses performed on the Transient-class and LLE data for source detection and duration estimation (Section 3.3), unbinned maximum likelihood analysis performed on the Transient-class data for source detection, spectral fitting, localization (Section 3.2), and a spectral fitting analysis performed jointly on the LAT Transient-class and the GBM data (Section 3.4).
• Details of the implementation of the analysis sequence are given in Section 3.5.

3.1.1. LAT

• The background in the LAT data is composed of charged cosmic rays (CRs) misclassified as γ -rays, astrophysical γ -rays coming from Galactic and extragalactic diffuse and point sources, and γ -rays from the Earth’s limb produced by interactions of CRs in the upper atmosphere.
• The backgrounds for the Transient-class and LLE data are dominated by the CR component, while for the cleaner Diffuse class the backgrounds are dominated by astrophysical γ -rays.
• Note that the BKGE cannot estimate the backgrounds from the Earth’s limb.
• Finally, the fit parameters allow us to compute the background rate at any time during the burst and the authors use the covariance matrix from the fit to evaluate the uncertainty of this prediction.
• The two prescriptions gave very similar results in all cases.

3.1.2. GBM

• The authors use the GBM CSPEC event data from before and after the GRB prompt phase to obtain a model for the background, similar to the procedure followed for the LLE data above.
• After each fit, the authors check by eye that the residuals are consistent with the statistical fluctuations.
• In order to minimize the statistical and systematic errors (and hence ensure a reliable background estimate), the off-pulse time intervals must be close to the GRB’s signal, have a long enough duration, and also possibly have a smooth part of the light curve without bumps or other structures.
• In some cases, even with high-order polynomials, fitting the model to the background can be difficult and even impossible without being completely arbitrary .
• These issues are not solvable at present given their current understanding of the detectors and their backgrounds.

3.2. Maximum Likelihood Analysis

• The authors perform an unbinned maximum likelihood analysis using the tools in the Fermi ScienceTools software package, version 09-26-02.79.
• An overview of the method and its application for this study is given below.
• The fitting in the Likelihood tools is performed using an underlying engine such as MINUIT80 to perform the maximization.
• The authors cannot apply a similar unbinned maximum likelihood analysis to the LLE data, since the PSF, energy dispersion, effective area for the LLE events, and the expected backgrounds are not adequately known and/or verified yet.
• To avoid increasing the number of free parameters, the authors keep the normalization of the template for Galactic diffuse emission fixed to 1 for the analyses based on both event classes.

3.2.1. Source Detection

• To determine the significance of the detections of sources using the maximum likelihood analysis, the authors consider the “Test Statistic” (TS) equal to twice the logarithm of the ratio of the maximum likelihood value produced with a model including the GRB over the maximum likelihood value of the null hypothesis, i.e., a model that does not include the GRB.
• The PDF in such a source-overbackground model cannot, in general, be described by the usual asymptotic distributions expected from Wilks’ theorem (Wilks 1938; Protassov et al. 2002).
• It has been verified by dedicated Monte Carlo simulations (Mattox et al. 1996) that the cumulative PDF of the TS in the null hypothesis (i.e., the integral of the TS PDF from some TS value to infinity) is approximately equal to a χ2ndof /2 distribution, where ndof is the number of degrees of freedom (dof) associated with the GRB.
• In practice, the steps of detection and localization are iterated many times and a detection step is performed using an ROI centered on the position found by a prior localization step.
• For this reason, the authors expect some deviation from (1/2)χ24 distribution.

3.2.2. Localization

• The authors compute the localizations with the LAT in two steps.
• The first step provides a coarse estimation of the GRB position and is performed using the Fermi ScienceTool gtfindsrc.
• At this stage, the authors look for an excess consistent with the LAT PSF and they do not assume a particular background model.
• It assumes that the likelihood function is parabolic and symmetric in azimuth around the found position and so the provided localization error can be slightly underestimated.
• Therefore, this step is only used to obtain an initial seed for the follow-up analysis.

3.2.3. Event Probability

• The authors estimate the probability of each γ -ray being associated with the GRB by using the Fermi ScienceTool gtsrcprob.
• The probabilities are assigned via likelihood analysis and are computed starting from the best-fit model.
• In general, the predicted count density is the sum of the different contributions Si( , p, t), including the extended backgrounds (such as the isotropic component and the Galactic diffuse emission), background point sources (nearby bright sources), and the GRB under study.
• Because the flux varies with time, the authors perform the calculation in several time bins so that the flux is never averaged over long time intervals.
• The authors tested schemes for defining the time intervals including linear, logarithmic, and Bayesian-blocks (Scargle et al. 2013) binnings and the results were stable among the different choices.

3.3. Event Counting Analyses

• As discussed in the previous section, the effective area of the Transient class decreases strongly for off-axis angles greater than ∼70◦ or for energies less than ∼100 MeV.
• For this reason, in addition to the maximum likelihood analysis applied to Transient-class data described above, the authors search for sources using the LLE class.
• This class provides a significantly larger effective area below 100 MeV and a wider acceptance, although with a higher background level.
• The authors use it to obtain another duration measurement as well, which is dominated by events below 100 MeV and is thus complementary to the duration measurement obtained with Transient-class data.

3.3.1. Source Detection Using LLE Data

• The pair δt, t0 corresponds to the highest sensitivity to the signal of this particular GRB.
• The significance S in each bin is thus derived from the Poisson probability of obtaining the observed number of counts given the expectation from the background, by converting this probability to an equivalent sigma level for a one-sided standard normal distribution.
• For each of these 10 × 11 light curves, the background function b(t) is fit to the data outside the GRB window (as described in Section 3.1) and the algorithm seeks the bin with the largest significance S inside the GRB window.
• This new probability is converted to a Gaussian-equivalent significance S ′ and the pre-trials significance for the detection of the GRB is defined as Spre = max(S ′), where the maximum is computed over the 110 light curves.
• Since the data have been rebinned multiple times, a post-trial probability is finally computed to account for these dependent trials.

3.3.2. Duration Measurement

• The authors describe the duration of a GRB detected by the LAT using the parameter T90 (Kouveliotou et al. 1993).
• The authors duration estimation method is based on the above simple prescription, but is also extended to estimate the statistical uncertainty of the results and accounts for the effects of effective area variations over time (for its application to the Transientclass events).
• At the end of each step, an algorithm checks for the presence of a plateau by searching for statistically significant increases in the average value of the points added last to the curve.
• After the durations of all the simulated light curves have been measured, the median and a (minimum-width) 68% containment interval are calculated for each distribution and used as their measurements and ±1σ errors.
• In some cases, a GRB observation can be interrupted before the GRB emission becomes too weak to be detectable (i.e., before reaching a plateau in the integral distribution).

3.4.1. Data Preparation

• The authors then use the Fermi Science Tool gtbin to extract the observed spectrum (source + background) from the GBM TTE data.
• Because the RSP2 file contains several response matrices corresponding to consecutive time intervals that in general are shorter than t1–t2, the authors sum the matrices of all the sub-intervals included in t1–t2 using an appropriate weighting scheme.
• The authors bin the LAT data in 10 logarithmically spaced energy bins between 100 MeV and 250 GeV and use an energy-dependent ROI as described in Section 2.1.1.
• The authors derive the observed spectrum and the response matrix using the Fermi Science Tools gtbin and gtrspgen.
• Note that for GRBs detected by the LLE photon counting analysis outside the LAT FoV, the authors used only GBM data for the spectral analysis.

3.4.2. Spectral Fit

• The authors load the spectra and response matrices in XSPEC v.12.7.82.
• The authors do not exclude any energy bin in the LAT spectrum, since 81 Available at: http://heasarc.nasa.gov/ftools.
• This likelihood function is derived from a joint probability distribution, obtained by modeling the spectral counts as a Poisson process and the background counts as a Gaussian process.
• This is a known issue with gradient-descent algorithms (Arnaud et al. 2011).
• If the fitting algorithm finds an even better minimum for the statistic while computing error contours for this set of parameters, the authors adopt that as the new putative best fit and restart the error computation, iterating the procedure until no new minimum is found.

3.4.3. Spectral Models

• Traditionally, GRB spectra have been described using the phenomenological “Band function” (Band et al. 1993) or a model consisting of a PL with an exponential cutoff (also called a “Comptonized model”).
• In one case, Fermi observed a high-energy cutoff that required the addition of an exponential cutoff to the PL component in the spectral model (Ackermann et al. 2011), for a total of three components (Band, power law, and exponential cutoff).
• Here are the definitions of their additional model components: 1. Power law: N (E) ≡ kE−α , where α is the photon index.
• For such spectra, the multiplicative factors are unconstrained during the fit and therefore the authors removed them.

3.4.4. Definition of a Good Fit and Model Selection

• The main focus of the spectral analysis performed here is to characterize the GRB spectrum, which requires selecting the most appropriate spectral model.
• Each realization rip0 is obtained by adding Poisson noise to the count spectrum obtained by summing the observed background spectra and m0( p0).
• If S0 S1 and n0,dof = n1,dof , the two models are equivalent and the authors should report the results for both models.
• In Figure 6, the authors plot this function for the three cases.
• The authors fix an arbitrary threshold at Pth(> ΔS) = 1 × 10−5, where the statistical error on the simulated distribution, visible toward the tail, is still low.

3.5. Analysis Sequence

• The authors start their analysis using the best available localization provided via GCN typically by Swift or the GBM and in some cases by other observatories.
• Typically, the authors repeat the analysis 2–3 times until the localization obtained in the last step is within the error on the localization of the previous iteration.
• The time interval between the beginning of the first and the end of the last time bin for which TS > 16, named the “LAT temporally extended time interval” (hereafter “LATTE”), constitutes a rough estimate of the time window where the GRB emission is detectable with at least a ∼3σ significance.
• To obtain reliable values from the fit, the authors required at least one positive detection after the peak flux (in addition to ULs).
• The authors now perform the likelihood analysis on different time intervals, defined in Table 1.

4. RESULTS

• Any ULs from the maximum likelihood analysis are for a 95% CL and are calculated using a photon index of 2.
• The authors quote fluences in two Earth reference frame energy ranges: 10 keV–1 MeV and 100 MeV–10 GeV, appropriate to characterize the GRB emission as measured by the GBM and the LAT, respectively.
• For all of the quantities, a subscript (“LAT,” “GBM,” or “EXT”) is added to indicate the time interval used to perform the spectral analysis.
• A discussion on how the LATdetected burst fluences compare with the distribution of fluences for all the GBM-detected bursts is left for the next section.

4.1. LAT Detections

• The authors searched for high-energy emission with the LAT for the 733 GRBs described in Section 2.2 and detected 35, using the detection criteria described in Sections 3.3.1 and 3.2.1.
• Among the GCN circulars issued by the LAT team, three GRBs (listed below) were not included in this catalog as they were below the significance threshold, while the authors also discovered four not previously claimed bursts (GRBs 090227B, 090531B, 100620A, and 101123A).
• For the fake triggers, the authors did not obtain any value for the TS greater than TSmin = 20 (our nominal detection threshold).
• Whereas the GBM light curve is a broad single pulse event lasting ∼17 s, the LLE light curve shows a narrow spike at T0 that is not associated with the main pulse in the GBM, with a low significance of 3.1σ only.
• In order to minimize contamination from the bright limb of the Earth, the authors rejected any data taken during intervals for which the ROI intersected the Earth’s limb, a cut that is more conservative than requiring that the GRB is not occulted by the Earth.

4.2. Emission Onset Time and Duration in the LAT

• The authors applied their duration measurement algorithms to all of the significantly detected GRBs, as described in Section 3.3.2.
• Referring to the durations reported in the GBM GRB catalog (Paciesas et al. 2012), the authors report in the second column whether the GRB was categorized as long (L) or short (S), as determined from the measured T90 in the 50 keV–300 keV energy bands.
• On the other hand, the bottom panels of both figures show that the durations measured using the LLE data are in better agreement with those measured by the GBM.
• This can partially explain the systematically longer durations (T90) estimated using the LAT Transient-class events, but would not explain the systematically later onset times (T05).
• The authors also note that a possible selection effect could arise owing to the typical GRB off-axis angles at the trigger time.

4.3. Maximum Likelihood Analysis

• The authors split GRB observations into the six time intervals listed in Table 1 and performed a LAT-only spectral analysis using the maximum likelihood technique described in Section 3.2.
• Since in the “PRE” interval the GRB is not detectable (by construction), the authors omit reporting results from this interval and they focus on the five remaining time windows.
• The results of this analysis, namely the TS, the best-fit photon index, and the flux and fluence for the 100 MeV–10 GeV energy range are presented in Table 4.
• When possible, the authors also compute 18 The Astrophysical Journal Supplement Series, 209:11 (90pp), 2013 November 1 Ackermann et al.
• The isotropic equivalent energy Eiso in the 100 MeV–10 GeV rest-frame energy band.

4.3.1. Fluxes and Fluences

• Figure 11 shows the flux and fluence measured by the LAT in the “GBM” (top two panels) and “LAT” (bottom two panels) time intervals as a function of the durations of these time intervals (i.e., GBM and LAT T90, respectively).
• The fluxes and fluences presented in these figures are for the 100 MeV–10 GeV energy range.
• As can be interestingly seen in the bottom right panel of Figure 11, within the first 3 years of operations the LAT detected four very high fluence bursts (GRBs 080916C, 090510, 090902B, and 090926A) that are outliers with respect to the main distribution of the LAT-detected GRBs.
• The authors will revisit these hyper-fluent bursts in Section 5.2, where they discuss the energetics of Fermi-LAT detected GRBs.

4.3.2. LAT Localizations

• The authors evaluate localizations from the LAT for all GRBs detected by the maximum likelihood analysis by searching for the maximum of the TS map according to the procedure described in Section 3.2.2.
• The authors present their results in Table 5, in which they report the position of the maximum of the TS map (i.e., the LAT localization) along with its 68%, 90%, and 95% statistical errors.

4.3.3. High-energy Photon Events

• The authors report the energies and arrival times of a set of interesting high-energy photons that, according to their likelihood analysis (as described in Section 3.2.3), have a high probability (P > 0.9) of being associated with the GRBs.
• These results show that the detection of high-energy events with GRB point source probabilities P > 0.9 is not strongly correlated with features in the GBM light curve.
• GRB 100728A is particularly interesting since a 13.54 GeV event was detected ∼90 minutes after the trigger time.

4.3.4. Temporally Extended Emission

• To study the temporal decay of the extended emission detected by the LAT, the authors utilized the time-resolved analysis described in Section 3.5.
• Flux tp, quantities shown in the two top panels of Figure 14.
• As a result, it is more precise (i.e., with a smaller uncertainty) for bright GRBs than for faint GRBs.
• The four most luminous bursts detected by the LAT have some of the highest peak fluxes in the ensemble, all exceeding 10−3 cm−2 s−1.
• For all other GRBs, the authors report the decay index for the whole extended emission starting from the peak flux and the decay index for the light curve starting from the end of the low-energy (GBM) emission.

4.4. Joint GBM-LAT Spectral Fits

• For each GRB detected with the LAT, the authors performed joint GBM-LAT spectral analyses in two time intervals, following the procedure described in Section 3.4.
• Results of this analysis for all the bursts detected by the LAT in Table 12.
• To elaborate on the table entries, consider the results of the time-integrated analysis reported in Table 11: the first entry refers to the spectrum of GRB 080825C, which is best described by a Band model, thus only the columns referring to the parameters of the Band model are filled, and only the total fluence is reported.
• Correspondingly, all columns for the parameters of those components are filled, as well as the columns for the total fluence and the fluences for the first component (Band) and the second component (PL with an exponential cutoff).
• GRB 101014A was detected too close to the Earth’s limb at the time of the trigger, resulting in a very low exposure for the LAT due to the zenith angle cut (see Section 2.1.1).

4.4.1. Extra Components

• The authors found that four GRBs clearly require a PL added to the Band spectrum in both time intervals that they studied.
• Each GRB is modeled by one main component and eventually one or more additional components.
• The spectrum of GRB 090926A is instead modeled by a Band model plus a power law times an exponential cutoff (see the main text).
• Finally, the spectral analysis of GRB 110731A by Ackermann et al. (2013) revealed a hint of another cutoff at high energy with a significance of ∼4σ in the time interval starting from the LAT T05 and ending at the GBM T95.

5.1.1. A Band Model Crisis?

• Before the launch of Fermi, GRBs were mainly studied in the energy range from a few keV to a few MeV, with the catalog of BATSE (Kaneko et al. 2006, 2008) constituting the largest sample available to date.
• For convenience, the authors also report their off-axis angles θ at the trigger times.
• Kaneko et al. (2006) found that the spectra of ∼85% of the brightest 350 BATSE GRBs are well described by a Band function, while the authors find that 70% of LAT-detected GRBs are well described by either a Band model or a Comptonized model, which is similar to a Band model with a very soft value of β.
• Given the small size of their sample, the two fractions are very similar.
• On the other hand, Table 10 shows that the spectra of all of the brightest bursts inside the LAT FoV present significant devi- ations from a Band function, requiring additional components.

5.2. Energetics

• Cenko et al. (2011) and Racusin et al. (2011) studied the energetics of the afterglows of LAT-detected GRBs and concluded that they are among the most luminous afterglows observed by Swift.
• The authors start their analysis by examining the properties of LAT-detected GRBs in the context of the prompt emission and compare the high-energy properties measured by the LAT to the low-energy properties measured by the GBM.

5.2.1. Prompt Phase Energetics

• The authors first study the fluence and then continue with the subsample of GRBs that have a measured redshift and examine intrinsic GRB quantities.
• The top panel of Figure 17 shows the fluence measured by the LAT versus the fluence measured by the GBM in the “GBM” time window.
• It is interesting to note that the three short LAT-detected bursts have a greater ratio of high- to low-energy fluence than the bulk of the long-GRB population (blue symbols).
• In addition, the authors compute the isotropic equivalent energy in a narrower band (1 keV–10 MeV), covering mainly the energy range of the GBM detectors.
• In Figure 20 (top panel), the authors try to address this issue by plotting the amount of energy radiated by the source between 100 MeV and 10 GeV during the temporal extended emission compared with that radiated in the wider 1 keV–10 GeV energy range in the “GBM” time interval.

5.2.2. The Highest Energy Photons

• Internal-opacity constraints also indicate that these high-energy photon detections require large bulk Lorentz factors for the jet.
• Finally, the short time delay observed in LAT GRBs between low- and high-energy events can be used to place tight constraints on any energy dependence of the speed of light in vacuum, as postulated by some quantum gravity theories (Abdo et al. 2009b).
• For long bursts, the most energetic photons appear in the brightest GRBs.
• More statistics are needed to determine whether this pattern is significant.

5.2.3. Extended Phase Energetics

• The authors have explored the energy budget of the highly energetic GRBs during the prompt phase.
• This implies that the energy released above 100 MeV during the prompt emission is similar to the energy released during the temporally extended emission.

5.3. High-energy Spectral Properties

• In the previous section, the authors discussed the energetics of FermiLAT GRBs and they now consider their spectral properties.
• To further explore whether the photon indices depend on duration, the authors plot in Figure 26 the value of the photon index of the extra PL as measured in the “GBM” time window ΓGBM (top panel) and in the “EXT” time window ΓEXT (bottom panel) versus the GBM T90.
• The value ΓEXT was obtained by their LAT-only likelihood analysis and the β value was obtained by their joint GBM-LAT spectral fits.
• Therefore, it is very reasonable that when the authors replace the Comptonized model with a Band function, the resulting β parameter is very steep and not constrained toward lower values.
• For the case of GRB 090926A, the extra PL component during the prompt emission is significantly attenuated at high energies and the model that best fits the emission during the “GBM” time window consists of a Band function plus a Comptonized model and has a very high peak energy.

5.4. Extended Emission Temporal Decay

• In Figure 28, the authors report the “late-time decay index” αL as a function of the fluence measured by the LAT in the GBM interval (top panel) and the luminosity in the “GBM” time interval (lower panel).
• The values of αL seem to cluster around 1, which, in the context of the fireball model, indicates an adiabatic expansion of the fireball (see Section 6.2).
• Observed only the first steep part of the decay after the prompt phase and that the authors cannot exclude the existence of a flattening or a break at later times that would reconcile them with the other bursts.

5.5. LAT Detection Rate

• Band et al. (2009) reported the number of expected GRBs per year detectable by the LAT as a function of the number of excess events.
• This calculation was performed using a standard survey profile without any pointed-mode observations (due to a positive response to ARR or planned Target Of Opportunity (TOO)).
• Additionally, the differences between the predicted and observed numbers of GRBs increase for bursts with many γ -rays in the LAT data.

5.6. Detectability of GBM Bursts

• The authors limit the current analysis to the competing effects that the effective area decreases with increasing off-axis angle θ while the solid angle increases with θ .
• Generally speaking, the LAT-detected GRBs are among the brightest GBM GRBs occurring in the LAT FoV.
• These cases highlight the importance of secondary considerations other than θ or fluence.
• In terms of GBM fluence, short bursts are easier to detect.
• This is explained by the results of the combined spectral analysis (summarized in Table 11), which show that the best-fit spectral model is a Comptonized model cutting off approximately at 1.2 MeV, implying suppression of high-energy emission.

6. INTERPRETATION

• The authors have characterized the high-energy emission observed from 35 GRBs detected by the LAT.
• Here, the authors discuss plausible interpretations of the emission properties observed with the LAT, the salient features of these models, and possible issues.

6.1. Fluence and Energetics of LAT Bursts

• It is evident that most of the LAT bursts do seem to be very bright in the GBM, especially when comparing their 10 keV–1 MeV fluences to the 8 keV–1 MeV fluence of the typical GBM bursts (Goldstein et al. 2012).
• The additional PL spectral component is most likely responsible for the high fluence detected by the LAT, as also indicated in Figure 24 for five of the eight brightest bursts.
• Racusin et al. (2011) showed that the redshift distributions are statistically consistent for Swift-BAT-detected GRBs, those detected by both GBM and BAT, and the small sample of LAT-detected bursts with measured redshifts.

6.2. Temporally Extended Emission

• The flux of LAT-detected emission at late times decays rather smoothly and can generally be fit with a PL Fν ∝ t−αL .
• Such behavior also is typically observed in X-ray, UV, and optical wavelengths after the prompt γ -ray emission and is attributed to the afterglow emission.
• The slightly larger values for the burst-averaged photon index in the earlier “GBM” time interval (ΓGBM = −2.08 ± 0.04) could be due to plausible contamination by the prompt emission in the LAT.
• In particular, the relation between the flux-decay slope α and spectral index β for the flux density Fν(t) ∝ t−αν−β varies between different parts of the spectrum.
• Thus, a simple interpretation of the αL ≈ 1 flux-decay index for most LAT bursts indicates that the 100 MeV emission is more likely from an adiabatic fireball (Kumar & Barniol Duran 2009; De Pasquale et al. 2010; Razzaque 2010) rather than from a radiative fireball, as Ghisellini et al. (2010) suggested.

6.3. Delayed Onset of LAT-detected Emission

• For most bursts, the onset of the LAT-detected emission, as measured by LAT T05 (100 MeV–10 GeV), is delayed with respect to the onset of the GBM-detected emission, measured by GBM T05 .
• The time required for the flux to increase and be detected by the LAT corresponds to the delayed onset of the LAT emission in this scenario.
• These estimates of Γ0 are similar to Γmin values calculated from γ γ pair production opacities for the four brightest LAT bursts (Abdo et al.
• The temporal variability of >100 MeV emission in GRBs 090902B (Abdo et al. 2009a) and 090926A (Ackermann et al. 2011) argues against a simple forward shock interpretation in the prompt phase, since such variability is characteristic of internal shocks.

6.4. Spectral Models of LAT-detected Emission

• This component is in addition to the Band function or the Comptonized model that typically describes the keV–MeV emission.
• In other bursts it can be softer and consequently not easily detectable.
• Whether or not the same hard PL component in the prompt phase evolves into the PL in the “EXT” time window is a central issue in GRB science.

6.5. Summary and Conclusions

• For each of these bursts, the authors have examined the spectral and temporal behavior of its high-energy emission.
• The authors have also compared the LAT-detected emission with the lower energy emission detected by the GBM for a much greater number of bursts and sought theoretical interpretations of the LAT observations.
• They are also the most energetic when redshift measurements allow the determination of their total luminosities.
• The spectra of LAT GRBs are typically well described by a PL with a fairly narrow distribution of indices, centered at −2.0 although deviations (spectral cutoffs) from a pure PL have been detected in GRBs 090926A and 110731A in the GeV range.
• The early afterglow model for temporally extended LAT-detected emission can explain both the delayed onset and the additional component, but other models involving internal shocks cannot be ruled out.

Figures

Table 4 Results from Likelihood Analysis

Table 14 (Continued)

Figure 69. Composite light curve for GRB 100116A: summed GBM/NaI detectors (first two panels), GBM/BGO (third panel), LLE (fourth panel), and LAT Transient-class events above 100 MeV within a 12◦ ROI (bottom panel). See Appendix B.1 for more information on lines and symbols in the LAT panels.

Figure 4. Duration estimation of GRB 080916C using Transient-class data. Curves: distributions of T05 (top), T95 (middle), and T90 (bottom) as measured from the simulations. Middle vertical dashed lines: median of the distributions, constituting our best estimate of the duration. Left- and right-hand vertical dashed lines: 68% containment intervals, constituting our estimated error for the duration.

Figure 3. Duration estimation of GRB 080916C using Transient-class data. Top: number of detected counts (black) and estimated background (red) per time interval. Middle: accumulated number of detected counts (black) and expected background (red) since the trigger time. Bottom: accumulated number of background-subtracted events.

Figure 49. Composite light curve for GRB 090328: summed GBM/NaI detectors (first two panels), GBM/BGO (third panel), LLE (fourth panel), and LAT Transient-class events above 100 MeV within a 12◦ ROI (bottom panel). See Appendix B.1 for more information on lines and symbols in the LAT panels.

Figure 12. Observed (upward-pointing triangles) and rest frame (downwardpointing triangles) energy and arrival time for the highest energy events associated with long (blue) and short (red) LAT-detected GRBs. Vertical dashed lines connect the observed and the rest frame energies for the same burst. Data points are from Table 8.

Figure 13. Top: the decay of the luminosity L with time measured in the rest frame for the three GRBs in which we detect a significant time break. Dashed–dotted lines are the best fits of the broken PL model for each GRB, while the dashed crosses are the luminosities before the peak times, which have not been used in the fits (see the text). Bottom: the same quantities for all the GRBs with detected extended emission.

Figure 92. Likelihood light curve for GRB 110625A (flux above 100 MeV on the left, photon index on the right). See Appendix B.1 for more information on lines and symbols.

Figure 28. Value of the “late-time decay index” αL as a function of the fluence between 100 MeV and 10 GeV in the “GBM” time interval (top) and the isotropic luminosity between 1 keV–10 GeV in the source frame (bottom). The value of αL is ∼1, except for GRB 080916C and GRB 110731A, which notably have the shortest durations when measured in the source frame (see the text). The symbol convention is the same as in Figure 9.

Figure 14. Quantities characterizing the extended high-energy emission detected by the LAT. Top: peak flux, middle: time of the peak flux, and bottom: temporal-decay index αL.

Table 9 Temporally Extended High-energy Emission

Figure 42. Likelihood light curve for GRB 081024B (flux above 100 MeV on the left, photon index on the right). See Appendix B.1 for more information on lines and symbols.

Figure 43. Composite light curve for GRB 090217: summed GBM/NaI detectors (first two panels), GBM/BGO (third panel), LLE (fourth panel), and LAT Transient-class events above 100 MeV within a 12◦ ROI (bottom panel). See Appendix B.1 for more information on lines and symbols in the LAT panels.

Figure 35. Composite light curve for GRB 080825C: summed GBM/NaI detectors (first two panels), GBM/BGO (third panel), LLE (fourth panel), and LAT Transient-class events above 100 MeV within a 12◦ ROI (bottom panel). See Appendix B.1 for more information on the lines and symbols in the LAT panels.

Figure 50. Background estimation in the LLE light curve of GRB 090328401. Gray and blue histograms are the count rates in the LAT detector for two different time binnings. The dashed gray line is the best-fit background.

Figure 51. Likelihood light curve for GRB 090328 (flux above 100 MeV on the left, photon index on the right). See Appendix B.1 for more information on lines and symbols.

Figure 83. Composite light curve for GRB 101014A: summed GBM/NaI detectors (first two panels), GBM/BGO (third panel) and LLE (bottom panel). See Appendix B.1 for more information on lines.

Figure 91. Composite light curve for GRB 110625A: summed GBM/NaI detectors (first two panels), GBM/BGO (third panel), LLE (fourth panel), and LAT Transient-class events above 100 MeV within a 12◦ ROI (bottom panel). See Appendix B.1 for more information on lines and symbols in the LAT panels.

Figure 56. Likelihood light curve for GRB 090626 (flux above 100 MeV on the left, photon index on the right). See Appendix B.1 for more information on lines and symbols.

Figure 76. Composite light curve for GRB 100620A: summed GBM/NaI detectors (first two panels), GBM/BGO (third panel), LLE (fourth panel), and LAT Transient-class events above 100 MeV within a 12◦ ROI (bottom panel). See Appendix B.1 for more information on lines and symbols in the LAT panels.

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The First Fermi-LAT Gamma-Ray Burst Catalog
Ackermann, M.; et al., [Unknown]; van der Horst, A.J.
DOI
10.1088/0067-0049/209/1/11
Publication date
2013
Document Version
Final published version
Published in
The Astrophysical Journal. Supplement Series
Link to publication
Citation for published version (APA):
Ackermann, M., et al., U., & van der Horst, A. J. (2013). The First Fermi-LAT Gamma-Ray
Burst Catalog.
The Astrophysical Journal. Supplement Series
,
209
(1), [11].
https://doi.org/10.1088/0067-0049/209/1/11
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The Astrophysical Journal Supplement Series, 209:11 (90pp), 2013 November 1 doi:10.1088/0067-0049/209/1/11
C
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2013. The American Astronomical Society. All rights reserved. Printed in the U.S.A.
THE FIRST FERMI-LAT GAMMA-RAY BURST CATALOG
M. Ackermann
1
, M. Ajello
2
,K.Asano
3
, M. Axelsson
4,5,6
, L. Baldini
7
, J. Ballet
8
, G. Barbiellini
9,10
, D. Bastieri
11,12
,
K. Bechtol
13
, R. Bellazzini
14
,P.N.Bhat
15
, E. Bissaldi
16
, E. D. Bloom
13
, E. Bonamente
17,18
, J. Bonnell
19,20
,
A. Bouvier
21
,T.J.Brandt
19
, J. Bregeon
14
, M. Brigida
22,23
,P.Bruel
24
, R. Buehler
13
, J. Michael Burgess
15
, S. Buson
11,12
,
D. Byrne
25
, G. A. Caliandro
26
, R. A. Cameron
13
, P. A. Caraveo
27
, C. Cecchi
17,18
, E. Charles
13
, R. C. G. Chaves
8
,
A. Chekhtman
28,69
, J. Chiang
13
, G. Chiaro
12
,S.Ciprini
29,30
,R.Claus
13
, J. Cohen-Tanugi
31
, V. Connaughton
15
,
J. Conrad
5,32,33,70
, S. Cutini
29,30
, F. D’Ammando
34
, A. de Angelis
35
, F. de Palma
22,23
, C. D. Dermer
36
,R.Desiante
9
,
S. W. Digel
13
,B.L.Dingus
37
, L. Di Venere
13
, P. S. Drell
13
, A. Drlica-Wagner
13
, R. Dubois
13
, C. Favuzzi
22,23
,
E. C. Ferrara
19
, G. Fitzpatrick
25
, S. Foley
25,38
, A. Franckowiak
13
, Y. Fukazawa
39
,P.Fusco
22,23
, F. Gargano
23
,
D. Gasparrini
29,30
, N. Gehrels
19
, S. Germani
17,18
, N. Giglietto
22,23
, P. Giommi
29
, F. Giordano
22,23
, M. Giroletti
34
,
T. Glanzman
13
, G. Godfrey
13
, A. Goldstein
15
, J. Granot
40
,I.A.Grenier
8
, J. E. Grove
36
, D. Gruber
38
,S.Guiriec
19
,
D. Hadasch
26
, Y. Hanabata
39
, M. Hayashida
13,41
, D. Horan
24
,X.Hou
42
,R.E.Hughes
43
, Y. Inoue
13
, M. S. Jackson
5,6
,
T. Jogler
13
,G.J
´
ohannesson
44
,A.S.Johnson
13
,W.N.Johnson
36
, T. Kamae
13
, J. Kataoka
45
, T. Kawano
39
, R. M. Kippen
37
,
J. Kn
¨
odlseder
46,47
, D. Kocevski
13
, C. Kouveliotou
48
,M.Kuss
14
,J.Lande
13
,S.Larsson
4,5,32
, L. Latronico
49
,S.-H.Lee
50
,
F. Longo
9,10
, F. Loparco
22,23
, M. N. Lovellette
36
, P. Lubrano
17,18
,F.Massaro
13
, M. Mayer
1
, M. N. Mazziotta
23
,
S. McBreen
25,38
,J.E.McEnery
19,20
, S. McGlynn
51
, P. F. Michelson
13
,T.Mizuno
52
, A. A. Moiseev
20,53
, C. Monte
22,23
,
M. E. Monzani
13
, E. Moretti
5,6
, A. Morselli
54
, S. Murgia
13
,R.Nemmen
19
,E.Nuss
31
, T. Nymark
5,6
, M. Ohno
55
,
T. Ohsugi
52
, N. Omodei
13
, M. Orienti
34
, E. Orlando
13
, W. S. Paciesas
56
, D. Paneque
13,57
, J. H. Panetta
13
, V. Pelassa
15
,
J. S. Perkins
19,53,58,59
, M. Pesce-Rollins
14
, F. Piron
31
, G. Pivato
12
, T. A. Porter
13
, R. Preece
15
,J.L.Racusin
19
,
S. Rain
`
o
22,23
, R. Rando
11,12
,A.Rau
38
, M. Razzano
14,21
, S. Razzaque
60
,A.Reimer
13,16
,O.Reimer
13,16
, T. Reposeur
42
,
S. Ritz
21
, C. Romoli
12
, M. Roth
61
, F. Ryde
5,6
, P. M. Saz Parkinson
21
, T. L. Schalk
21
, C. Sgr
`
o
14
,E.J.Siskind
62
,
E. Sonbas
19,56,63
, G. Spandre
14
, P. Spinelli
22,23
,D.J.Suson
64
,H.Tajima
13,65
, H. Takahashi
39
, Y. Takeuchi
45
, Y. Tanaka
55
,
J. G. Thayer
13
, J. B. Thayer
13
, D. J. Thompson
19
, L. Tibaldo
13
, D. Tierney
25
, M. Tinivella
14
, D. F. Torres
26,66
,
G. Tosti
17,18
,E.Troja
19,71
, V. Tronconi
12
,T.L.Usher
13
, J. Vandenbroucke
13
, A. J. van der Horst
48,71
, V. Vasileiou
31
,
G. Vianello
13,67
, V. Vitale
54,68
, A. von Kienlin
38
,B.L.Winer
43
,K.S.Wood
36
, M. Wood
13
,S.Xiong
15
, and Z. Yang
5,32
1
Deutsches Elektronen Synchrotron DESY, D-15738 Zeuthen, Germany
2
Space Sciences Laboratory, 7 Gauss Way, University of California, Berkeley, CA 94720-7450, USA
3
Interactive Research Center of Science, Tokyo Institute of Technology, Meguro City, Tokyo 152-8551, Japan
4
Department of Astronomy, Stockholm University, SE-106 91 Stockholm, Sweden
5
The Oskar Klein Centre for Cosmoparticle Physics, AlbaNova, SE-106 91 Stockholm, Sweden
6
Department of Physics, Royal Institute of Technology (KTH), AlbaNova, SE-106 91 Stockholm, Sweden
7
Universit
`
a di Pisa and Istituto Nazionale di Fisica Nucleare, Sezione di Pisa I-56127 Pisa, Italy
8
Laboratoire AIM, CEA-IRFU/CNRS/Universit
´
e Paris Diderot, Service d’Astrophysique, CEA Saclay, 91191 Gif sur Yvette, France
9
Istituto Nazionale di Fisica Nucleare, Sezione di Trieste, I-34127 Trieste, Italy
10
Dipartimento di Fisica, Universit
`
a di Trieste, I-34127 Trieste, Italy
11
Istituto Nazionale di Fisica Nucleare, Sezione di Padova, I-35131 Padova, Italy
12
Dipartimento di Fisica e Astronomia “G. Galilei,” Universit
`
a di Padova, I-35131 Padova, Italy
13
W. W. Hansen Experimental Physics Laboratory, Kavli Institute for Particle Astrophysics and Cosmology, Department of Physics and SLAC National Accelerator
Laboratory, Stanford University, Stanford, CA 94305, USA; nicola.omodei@stanford.edu, giacomov@slac.stanford.edu
14
Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
15
Center for Space Plasma and Aeronomic Research (CSPAR), University of Alabama in Huntsville, Huntsville, AL 35899, USA
16
Institut f
¨
ur Astro- und Teilchenphysik and Institut f
¨
ur Theoretische Physik, Leopold-Franzens-Universit
¨
at Innsbruck, A-6020 Innsbruck, Austria
17
Istituto Nazionale di Fisica Nucleare, Sezione di Perugia, I-06123 Perugia, Italy
18
Dipartimento di Fisica, Universit
`
a degli Studi di Perugia, I-06123 Perugia, Italy
19
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
20
Department of Physics and Department of Astronomy, University of Maryland, College Park, MD 20742, USA
21
Santa Cruz Institute for Particle Physics, Department of Physics and Department of Astronomy and Astrophysics,
University of California at Santa Cruz, Santa Cruz, CA 95064, USA
22
Dipartimento di Fisica “M. Merlin” dell’Universit
`
a e del Politecnico di Bari, I-70126 Bari, Italy
23
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy
24
Laboratoire Leprince-Ringuet,
´
Ecole Polytechnique, CNRS/IN2P3, Palaiseau, France
25
University College Dublin, Belfield, Dublin 4, Ireland
26
Institut de Ci
`
encies de l’Espai (IEEE-CSIC), Campus UAB, 08193 Barcelona, Spain
27
INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica, I-20133 Milano, Italy
28
Center for Earth Observing and Space Research, College of Science, George Mason University, Fairfax, VA 22030, USA
29
Agenzia Spaziale Italiana (ASI) Science Data Center, I-00044 Frascati (Roma), Italy
30
Istituto Nazionale di Astrofisica - Osservatorio Astronomico di Roma, I-00040 Monte Porzio Catone (Roma), Italy
31
Laboratoire Univers et Particules de Montpellier, Universit
´
e Montpellier 2, CNRS/IN2P3, Montpellier, France; piron@in2p3.fr, vlasios.vasileiou@lupm.in2p3.fr
32
Department of Physics, Stockholm University, AlbaNova, SE-106 91 Stockholm, Sweden
33
The Royal Swedish Academy of Sciences, Box 50005, SE-104 05 Stockholm, Sweden
34
INAF Istituto di Radioastronomia, 40129 Bologna, Italy
35
Dipartimento di Fisica, Universit
`
a di Udine and Istituto Nazionale di Fisica Nucleare, Sezione di Trieste, Gruppo Collegato di Udine, I-33100 Udine, Italy
36
Space Science Division, Naval Research Laboratory, Washington, DC 20375-5352, USA
37
Los Alamos National Laboratory, Los Alamos, NM 87545, USA
38
Max-Planck Institut f
¨
ur extraterrestrische Physik, 85748 Garching, Germany
1

The Astrophysical Journal Supplement Series, 209:11 (90pp), 2013 November 1 Ackermann et al.
39
Department of Physical Sciences, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8526, Japan
40
Department of Natural Sciences, The Open University of Israel, 1 University Road, POB 808, Ra’anana 43537, Israel
41
Department of Astronomy, Graduate School of Science, Kyoto University, Sakyo-ku, Kyoto 606-8502, Japan
42
Universit
´
e Bordeaux 1, CNRS/IN2p3, Centre d’
´
Etudes Nucl
´
eaires de Bordeaux Gradignan, 33175 Gradignan, France
43
Department of Physics, Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA
44
Science Institute, University of Iceland, IS-107 Reykjavik, Iceland
45
Research Institute for Science and Engineering, Waseda University, 3-4-1, Okubo, Shinjuku, Tokyo 169-8555, Japan
46
CNRS, IRAP, F-31028 Toulouse cedex 4, France
47
GAHEC, Universit
´
e de Toulouse, UPS-OMP, IRAP, Toulouse, France
48
NASA Marshall Space Flight Center, Huntsville, AL 35812, USA
49
Istituto Nazionale di Fisica Nucleare, Sezione di Torino, I-10125 Torino, Italy
50
Yukawa Institute for Theoretical Physics, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
51
Exzellenzcluster Universe, Technische Universit
¨
at M
¨
unchen, D-85748 Garching, Germany
52
Hiroshima Astrophysical Science Center, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8526, Japan
53
Center for Research and Exploration in Space Science and Technology (CRESST) and NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
54
Istituto Nazionale di Fisica Nucleare, Sezione di Roma “Tor Vergata,” I-00133 Roma, Italy
55
Institute of Space and Astronautical Science, JAXA, 3-1-1 Yoshinodai, Chuo-ku, Sagamihara, Kanagawa 252-5210, Japan
56
Universities Space Research Association (USRA), Columbia, MD 21044, USA
57
Max-Planck-Institut f
¨
ur Physik, D-80805 M
¨
unchen, Germany
58
Department of Physics and Center for Space Sciences and Technology, University of Maryland Baltimore County, Baltimore, MD 21250, USA
59
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA
60
University of Johannesburg, Department of Physics, University of Johannesburg, Auckland Park 2006, South Africa; soebur.razzaque@gmail.com
61
Department of Physics, University of Washington, Seattle, WA 98195-1560, USA
62
NYCB Real-Time Computing Inc., Lattingtown, NY 11560-1025, USA
63
Adıyaman University, 02040 Adıyaman, Turkey
64
Department of Chemistry and Physics, Purdue University Calumet, Hammond, IN 46323-2094, USA
65
Solar-Terrestrial Environment Laboratory, Nagoya University, Nagoya 464-8601, Japan
66
Instituci
´
o Catalana de Recerca i Estudis Avan¸cats (ICREA), Barcelona, Spain
67
Consorzio Interuniversitario per la Fisica Spaziale (CIFS), I-10133 Torino, Italy
68
Dipartimento di Fisica, Universit
`
a di Roma “Tor Vergata,” I-00133 Roma, Italy
Received 2013 March 11; accepted 2013 July 15; published 2013 October 23
ABSTRACT
In three years of observations since the beginning of nominal science operations in 2008 August, the Large Area
Telescope (LAT) on board the Fermi Gamma-Ray Space Telescope has observed high-energy (20 MeV) γ -ray
emission from 35 gamma-ray bursts (GRBs). Among these, 28 GRBs have been detected above 100 MeV and 7
GRBs above ∼20 MeV. The first Fermi-LAT catalog of GRBs is a compilation of these detections and provides
a systematic study of high-energy emission from GRBs for the first time. To generate the catalog, we examined
733 GRBs detected by the Gamma-Ray Burst Monitor (GBM) on Fermi and processed each of them using the
same analysis sequence. Details of the methodology followed by the LAT collaboration for the GRB analysis
are provided. We summarize the temporal and spectral properties of the LAT-detected GRBs. We also discuss
characteristics of LAT-detected emission such as its delayed onset and longer duration compared with emission
detected by the GBM, its power-law temporal decay at late times, and the fact that it is dominated by a power-law
spectral component that appears in addition to the usual Band model.
Key words: catalogs – gamma-ray burst: general – methods: data analysis
Online-only material: color figures
1. INTRODUCTION
Prior to the Fermi Gamma-Ray Space Telescope mission,
high-energy emission from gamma-ray bursts (GRBs) was
observed with the Energetic Gamma-Ray Experiment Tele-
scope (EGRET) covering the energy range from 30 MeV to
30 GeV (Hughes et al. 1980; Kanbach et al. 1988; Thompson
et al. 1993; Esposito et al. 1999) on board the Compton
Gamma-Ray Observatory (CGRO; 1991–2000) and, more re-
cently, by the Gamma-Ray Imaging Detector (GRID) on board
the Astro-rivelatore Gamma a Immagini LEggero spacecraft
(AGILE; Giuliani et al. 2008; Tavani et al. 2008, 2009). De-
spite the effective area and dead-time limitations of EGRET,
substantial emission above 100 MeV was detected for a few
69
Resident at Naval Research Laboratory, Washington, DC 20375, USA.
70
Royal Swedish Academy of Sciences Research Fellow, funded by a grant
from the K. A. Wallenberg Foundation.
71
NASA Postdoctoral Program Fellow, USA.
GRBs (Sommer et al. 1994; Hurley et al. 1994; Gonz
´
alez et al.
2003), suggesting a diversity of temporal and spectral proper-
ties at high energies. Of particular interest was GRB 940217, for
which delayed high-energy emission was detected by EGRET
up to ∼90 minutes after the trigger provided by CGRO’s Burst
And Transient Source Experiment (BATSE).
The Fermi observatory was placed into orbit on 2008 June 11.
It provides unprecedented breadth of energy coverage and
sensitivity for advancing the knowledge of GRB properties
at high energies. It has two instruments: the Gamma-Ray
Burst Monitor (GBM; Meegan et al. 2009) and the Large
Area Telescope (LAT; Atwood et al. 2009), which together
cover more than 7 decades in energy. The GBM comprises
twelve sodium iodide (NaI) and two bismuth germanate (BGO)
detectors sensitive in the 8 keV–1 MeV and 150 keV–40 MeV
energy ranges, respectively. The NaI detectors are arranged in
groups of three at each of the four edges of the spacecraft and
the two BGO detectors are placed symmetrically on opposite
2

The Astrophysical Journal Supplement Series, 209:11 (90pp), 2013 November 1 Ackermann et al.
sides of the spacecraft, resulting in a field of view (FoV) of
∼9.5 sr. Triggering and localization are determined from the NaI
detectors, while spectroscopy is performed using both the NaI
and BGO detectors. Localization is performed using the relative
event rates of detectors with different orientations with respect
to the source and is typically accurate to a few degrees. The
GBM covers roughly four decades in energy and provides a
bridge from the low energies (below ∼1 MeV), where most of
the GRB emission takes place, to the less explored energy range
that is accessible to the LAT.
The LAT is a pair-production telescope sensitive to γ -rays
in the energy range from ∼20 MeV to more than 300 GeV.
The instrument and its on-orbit calibrations are described in
detail in Atwood et al. (2009) and Abdo et al. (2009c). The
telescope consists of a 4 × 4 array of identical towers, each
including a tracker of silicon strip planes with foils of tungsten
converter interleaved, followed by a cesium iodide calorimeter
with a hodoscopic layout. This array is covered by a segmented
anti-coincidence detector of plastic scintillators that is designed
to efficiently identify and reject charged particle background
events. The wide FoV (∼2.4 sr at 1 GeV) of the LAT, its high
observing efficiency (obtained by keeping the FoV on the sky
with scanning observations), its broad energy range, its large
effective area (>1GeVis∼6500 cm
2
on-axis), its low dead time
per event (∼27 μs), its efficient background rejection, and its
good angular resolution (∼0.
◦
8 at 1 GeV) are vastly improved in
comparison with those of EGRET. As a result, the LAT provides
more GRB detections, higher statistics per detection, and more
accurate localizations (1
◦
).
Fermi has been routinely monitoring the γ -ray sky since 2008
August. From this time until 2011 August, when a new event
analysis (“Pass 7”; Ackermann et al. 2012a) was introduced, the
GBM detected about 730 GRBs, approximately half of which
occurred inside the LAT FoV. In ground processing, we search
for LAT counterparts to known GRBs, following each trigger
provided by the GBM and other instruments. In addition, we
also undertake blind searches for bursts not detected by other
instruments in the entire sample of LAT data, with however no
independent (i.e., not detected by other instruments) detections
so far.
Owing to the detection of temporally extended emission by
EGRET from GRB 940217 and the interest in studying GRB
afterglow emission at high energies, Fermi was designed with
the additional capability to repoint in the direction of a bright
GRB and keep its position near the center of the FoV of the
LAT (where the effective area to γ -rays is maximal) for several
hours (5 hr initially; 2.5 hr since 2010 November 23), subject to
Earth-limb constraints. This repointing occurs autonomously in
response to requests to the Fermi spacecraftfrom either the GBM
or the LAT (Autonomous Repoint Request, or ARR hereafter),
with adjustable brightness thresholds and has resulted in more
than 60 extended GRB observations between 2008 October 8,
when the capability was enabled, and 2011 August 1.
This article presents the first catalog of LAT-detected GRBs.
It covers a three-year period starting at the beginning of routine
science operations in 2008 August. In Section 2, we describe
the data used in this study and the list of GRB triggers that we
searched for LAT detections. In Section 3, we give a detailed
description of the analysis methods that we applied to detect and
localize GRBs with the LAT, as well as the methodology that we
followed to characterize their temporal and spectral properties.
In Sections 4 and 5, we present and discuss our results, with
a special emphasis both on the most interesting bursts and
on the common properties revealed by the LAT. The physical
implications of our observations are addressed in Section 6,
where we also discuss several open questions and topics of
interest for future analysis. In Appendix A, we investigate the
possible sources of systematic uncertainties via testing different
instrument response functions (IRFs) and configurations for the
analysis. Finally, in Appendix B, we discuss each individual
GRB in the catalog, reporting the details of its observation and
considering it in the context of multiwavelength observations.
2. DATA PREPARATION
In this section, we describe the data analyzed in this study and
the list of GRB triggers that we searched for LAT detections.
The results of this paper were produced using two sets
of LAT events corresponding to different quality levels and
corresponding IRFs in the event reconstruction: the Transient
event class (Atwood et al. 2009), which requires the presence
of a signal in both the tracker and the calorimeter of the LAT,
and the “LAT Low Energy” (LLE) event class (Pelassa et al.
2010), which requires a signal in only the tracker and essentially
consists of all the events that pass the on board γ filter having a
reconstructed direction (Ackermann et al. 2012a).
The LAT event classes underwent many stages of refine-
ment and were released as different versions (or “passes”)
of the data. This catalog uses the entire “Pass 6” event
dataset, in particular, the Pass 6 version 3 Transient event class
(“P6_V3_TRANSIENT”). The LAT team has switched from
using “Pass 6,” which had been used since the beginning of sci-
ence operations, to “Pass 7” data on the 1st of August 2011, the
end of the time period covered by this catalog.
As cross checks, we repeated some of the Transient-class
analyses using instead the “P6_V3_DIFFUSE” event class to
search for possible systematics that might arise from the choice
of event selection. Both the Transient and Diffuse classes offer
good energy and angular resolutions, along with large effective
areas above 100 MeV and reasonable residual background
rates.
72
The Diffuse class uses a very selective set of cuts to
keep the highest quality γ -ray candidates. As a result, it has a
relatively narrow point-spread function (PSF; 68% containment
radius of several degrees at 100 MeV and ∼0.
◦
25 at 10 GeV)
and a smaller background contamination with respect to the
Transient class. On the other hand, the Transient class, which
is defined with a less selective set of cuts, offers a significantly
larger effective area, especially below 1 GeV. The LLE class
corresponds to looser selection criteria, compared with the other
two classes, and is designed to provide a far larger effective area
at lower energies (especially below 100 MeV) and at larger off-
axis angles (especially above ∼60
◦
). The LLE PSF is wide (with
a 68% containment radius of ∼20
◦
, ∼13
◦
, and ∼7
◦
at 20 MeV,
50 MeV, and 100 MeV, respectively) and has a much higher
background contamination (∼300 Hz over the entire FoV) than
the other two event classes. Since the flux of a GRB is typically a
decreasing function of energy, the LLE class provides very good
statistics, which are useful for detailed studies of the temporal
structure of GRB emission. It also allows us to examine GRBs
with soft spectra or those that occur at a high off-axis angle,
which are not detectable by the other two event classes.
Our baseline LAT-only analysis (namely, localization, de-
tection, spectral fitting, and duration estimation) uses the
72
For more information on these event classes, see http://www.slac.
stanford.edu/exp/glast/groups/canda/archive/pass6v3/lat_Performance.htm.
3

The Astrophysical Journal Supplement Series, 209:11 (90pp), 2013 November 1 Ackermann et al.
Transient-class data. We use the LLE data only for source detec-
tion and duration measurement. As discussed above, the LAT
Diffuse data are used only as a cross-check of some of the
analysis results for the Transient class.
We perform joint GBM-LAT spectral fitting using the LAT
Transient-class data, the GBM Time-Tagged Event (TTE)
data and the GBM RSP/RSP2 response files.
73
We also
use GBM CSPEC data to produce our background model
(see Section 3.1.2).
All of our analyses also use the LAT FT2 data, which contain
information on the pointing history and the location of the Fermi
spacecraft around the Earth. We use FT2 files with 1 s binning.
2.1. Data Cuts
2.1.1. LAT Data
We select Transient class data with reconstructed energies
in the 100 MeV–100 GeV range. The lower limit is chosen to
reject events with poorly reconstructed directions and energies.
Moreover,for Pass 6, the LAT response is not adequately verified
at E<100 MeV energies and the contamination from cosmic
rays misclassified as gamma-rays is also significantly increased.
The upper limit (UL) was chosen at 100 GeV since we do not
expect to detect GRB photons at such high energies due to the
opacity of the universe and the limited effective area of the
LAT. We select events in a circular region of interest (ROI)
that is centered on the best available GRB localization. The
LAT PSF depends on the event energy and off-axis angle and
has been studied using Monte Carlo simulations. We use the
resulting description of the PSF to increase the sensitivity of
our analyses. For the event-counting and joint spectral-fitting
analyses, we select a variable ROI radius that depends on the
event energy and the off-axis angle of the GRB in such a way
as to select almost all the events consistent with the position of
the GRB given our PSF while rejecting much of the residual
cosmic-ray background, increasing the signal-to-noise ratio of
the selected data. To accomplish this, we split the events in
logarithmically spaced bins in energy and for each bin we select
only the events contained in an ROI around the source having
a radius corresponding to the 95% containment radius of the
PSF evaluated at an energy equal to the geometric mean of the
bin’s energy range. For the duration estimation using Transient
data, we deal with longer time periods, thus we dynamically
adjust the radii of the energy-dependent ROIs to follow the
variation of the off-axis angle with time. On the other hand, for
the LLE duration estimations and the joint GBM-LAT spectral
analyses, we use a single set of radii calculated using the PSF
corresponding to the GRB off-axis angle at trigger time. The
exact dependence of the LLE PSF on the off-axis angle is
not available yet. Instead, only two possible LLE PSFs are
available for setting the ROI radii: one for observations with
off-axis angles greater than 40
◦
and the other for observations
closer to the center of the FoV. Finally, for cases for which the
GRB localization error is non-negligible (i.e., for GBM or LAT
localizations), we increase the radius of each ROI by setting it
equal to the sum in quadrature of the localization error and the
95% containment radius of the PSF. For GRBs localized by the
Fermi GBM, we also added in quadrature a 3
◦
systematic error.
The maximum-likelihood analysis utilizes the PSF information
internally while calculating the probability of each event being
associated with the GRB, thus no optimization of the ROI radius,
73
All available from the Fermi Science Support Center (FSSC):
http://fermi.gsfc.nasa.gov/ssc/data/access/gbm/.
as above, is necessary. For the maximum-likelihood analyses,
we use a fixed-radius ROI set at 12
◦
, a value larger than the 99%
containment radius of the Transient LAT PSF evaluated for a
100 MeV event on axis.
We apply a cut to limit the contamination from γ -rays
produced by interactions of cosmic rays with the Earth’s upper
atmosphere. For our maximum-likelihood analysis, we use the
Fermi Science Tool gtmktime
74
to select only the time intervals
(the “Good Time Intervals” or GTIs) in which no portion of
the ROI is too close to the Earth’s limb. Because the Earth’s
limb lies at a zenith angle of 113
◦
and we wish to take into
account the finite angular resolution of the detector, we exclude
any events taken when the ROI is closer than 8
◦
to the Earth’s
limb or equivalently when it intersects the fiducial line at 105
◦
from the local zenith. For special cases, when the position of
the GRB is very close to the Earth’s limb, we compensate for
the loss of exposure due to this cut by reducing the size of the
ROI and simultaneously increasing the maximum zenith angle
to 110
◦
. This increases the duration of the GTI significantly,
allowing deeper exposures for searches of late γ -ray activity.
For all the other analyses (namely, event-counting analyses and
joint spectral fitting), we do not apply a cut to select GTIs as
above, but rather we process the whole observation and instead
reject individual events reconstructed farther than 105
◦
from the
local zenith.
2.1.2. GBM Data
The response of a GBM detector depends on the continuously
varying position of the GRB in its FoV, with its effective
area decreasing as the angular distance between the detector
boresight and the source (θ
GBM
) increases. Because of this, when
θ
GBM
is large, any systematic effects due to imperfect modeling
of the spacecraft or the individual detectors become relatively
important (Goldstein et al. 2012). For this reason, we use the
data from the GBM NaI detectors that have angles θ
GBM
< 50
◦
at the time of the trigger and the BGO detector facing the GRB
at the time of the trigger.
We also exclude any detector occulted by other detectors or
the spacecraft during any part of the analyzed time interval, as
advised in Goldstein et al. (2012).
Since θ
GBM
usually changes with time, the GBM Collabora-
tion released RSP2 files that contain several response matrices
corresponding to short consecutive time intervals (every 2
◦
of
slew of the detector about the source). With a suitable weight-
ing scheme, as described in Section 3.4.1, these files provide an
adequate description of the GRB detector responses.
Finally, in some cases, bright GRBs trigger an ARR, causing
rapid variations of θ
GBM
with time for some of the GBM detec-
tors. These variations create further variations in those detector
responses and background rates. In fact, due to its orbital and
angular dependence, the background of those detectors can be
very hard to predict. Also, since the RSP2 files might not be
binned finely enough in time to cover these rapid variations, we
excluded data from detectors that have such rapid variations.
2.2. Input GRB List
To search for GRBs in the LAT data, we use as input a
list comprising 733 bursts that triggered the GBM from 2008
August 4 to 2011 August 1 (GBM triggers bn080804456 to
bn110731465). We use the localizations provided by the GBM,
74
http://www.slac.stanford.edu/exp/glast/wb/prod/pages/sciTools_gtmktime/
gtmktime.htm
4

Frequently asked questions

Q1. What are the contributions mentioned in the paper "The first fermi-lat gamma-ray burst catalog" ?

The first Fermi-LAT catalog of GRBs is a compilation of these detections and provides a systematic study of high-energy emission from GRBs for the first time. To generate the catalog, the authors examined 733 GRBs detected by the Gamma-Ray Burst Monitor ( GBM ) on Fermi and processed each of them using the same analysis sequence. Details of the methodology followed by the LAT collaboration for the GRB analysis are provided. The authors also discuss characteristics of LAT-detected emission such as its delayed onset and longer duration compared with emission detected by the GBM, its power-law temporal decay at late times, and the fact that it is dominated by a power-law spectral component that appears in addition to the usual Band model. 

Q2. What is the probability of obtaining a S greater than the observed value?

At the end of the simulation, the distribution for ΔS is used to compute the probability P of obtaining a ΔS greater than the observed value, which corresponds to the complement of the cumulative distribution function. 

Q3. Why do the authors perform the calculation in several time bins?

Because the flux varies with time, the authors perform the calculation in several time bins so that the flux is never averaged over long time intervals. 

Q4. How long does the flux from a coasting fireball take to decelerate?

The bolometric flux from a coasting fireball increases as ∝ t2 (Sari 1997), both for an adiabatic and a radiative fireball, before it decelerates and enters a self-similar phase (Blandford & McKee 1976; Rees & Meszaros 1994). 

Q5. How do the authors obtain the response of a GBM detector in the interval to be analyzed?

The authors obtain the response of a GBM detector in the interval to be analyzed (t1–t2) using the RSP2 file for the detector for the time interval. 

Q6. How many BATSE GRBs require the more complex SBPL model?

Kaneko et al. (2006) found that 5% of BATSE GRBs require the more complex SBPL model, while no LAT-detected GRB requires it. 

Q7. Why is the duration of a GRB different?

Because of the unavoidable statistical fluctuations involved in the process of detecting incoming GRB flux, a GRB observed under identical conditions by a number of identical detectors will in general produce different detected light curves and hence different duration estimates. 

Q8. How do the authors compensate for the loss of exposure due to the cut?

For special cases, when the position of the GRB is very close to the Earth’s limb, the authors compensate for the loss of exposure due to this cut by reducing the size of the ROI and simultaneously increasing the maximum zenith angle to 110◦. 

Q9. What is the significance of the detections of sources using the maximum likelihood analysis?

To determine the significance of the detections of sources using the maximum likelihood analysis, the authors consider the “Test Statistic” (TS) equal to twice the logarithm of the ratio of the maximum likelihood value produced with a model including the GRB over the maximum likelihood value of the null hypothesis, i.e., a model that does not include the GRB. 

Q10. Why did the authors exclude data from detectors that have such rapid variations?

since the RSP2 files might not be binned finely enough in time to cover these rapid variations, the authors excluded data from detectors that have such rapid variations. 

Q11. What is the probability that a photon is produced by a component i?

In particular, the probability that a photon is produced by a component i is proportional to Mi, given byMi( ′, p′, t) = ∫ d dp Si( , p, t) R( , p; ′, p′, t), (2)where Si( , p, t) is the predicted counts density from the component at energy , position p, and (observed) time t, and the integral is the convolution over the instrument response R( , p; ′, p′, t). 

Q12. What is the index of PL flux decay?

The index of PL flux decay (the later index in the case of broken PL fits) is typically close to Fν ∝ t−1, with only a few exceptions. 

Q13. What is the expected trend for prompt emission bursts?

The five such bursts follow an expected trend: the more important the PL component in the prompt emission phase, the brighter the late-time emission becomes compared with the prompt high-energy γ -ray emission. 

Q14. What is the best-fit model for the GBM bursts?

In fact, for these bursts, the best-fit models found by their procedure were the Comptonized + power law and the Comptonized alone, respectively. 

Q15. What is the flux decay in a particular energy band?

The flux decay in a particular energy band is more complicated and depends on the fast- or slow-cooling spectral models (Sari et al. 1998), as well as on the surrounding environment (i.e., whether it is a uniform density interstellar medium (ISM) or whether a wind-type density profile is present (Sari et al. 

Q16. What is the flux of LAT-detected emission at late times?

The flux of LAT-detected emission at late times decays rather smoothly and can generally be fit with a PL Fν ∝ t−αL (see Section 4.3.4 and Figures 13 and 14). 

Q17. How much energy is radiated by the Band component during the prompt phase?

As shown, the energy radiated during the prompt emission by the PL component is between 10% and 50% of the energy radiated by the Band component.