J/ApJS/254/35ivo://CDS.VizieR/J/ApJS/254/35doi:10.26093/cds/vizier.22540035CDSLi L.Ryde F.Pe'er A.Yu H.-F.Acuner Z.2022-01-19T13:11:51Z2021-10-25T08:28:12ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frgamma-ray-astronomygamma-ray-burstsGamma-ray bursts (GRBs) are highly variable and exhibit strong spectral evolution. In particular, the emission properties vary from pulse to pulse in multipulse bursts. Here we present a time-resolved Bayesian spectral analysis of a compilation of GRB pulses observed by the Fermi/Gamma-ray Burst Monitor. The pulses are selected to have at least four time bins with a high statistical significance, which ensures that the spectral fits are well determined and spectral correlations can be established. The sample consists of 39 bursts, 117 pulses, and 1228 spectra. We confirm the general trend that pulses become softer over time, with mainly the low-energy power-law index {alpha} becoming smaller. A few exceptions to this trend exist, with the hardest pulse occurring at late times. The first pulse in a burst is clearly different from the later pulses; three-fourths of them violate the synchrotron line of death, while around half of them significantly prefer photospheric emission. These fractions decrease for subsequent pulses. We also find that in two-thirds of the pulses, the spectral parameters ({alpha} and peak energy) track the light-curve variations. This is a larger fraction compared to what is found in previous samples. In conclusion, emission compatible with the GRB photosphere is typically found close to the trigger time, while the chance of detecting synchrotron emission is greatest at late times. This allows for the coexistence of emission mechanisms at late times.2021ApJS..254...35Lhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJS/254/35CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toJ/ApJS/166/298 : Spectral cat. of bright BATSE gamma-ray bursts (Kaneko+, 2006)J/ApJ/720/1146 : Spectral analysis of GRBs (Lu+, 2010)J/ApJ/740/104 : BATSE GRB pulse catalog - preliminary data (Hakkila+, 2011)J/ApJ/756/112 : Fermi/GBM GRB time-resolved spectral analysis (Lu+, 2012)J/MNRAS/454/L31 : GRB prompt emission fitted with DREAM (Ahlgren+, 2015)J/A+A/573/A81 : Spectral properties of energetic GRBs (Yu+, 2015)J/A+A/588/A135 : Fermi/GBM GRB time-resolved spectral catalog (Yu+, 2016)J/ApJ/855/101 : BATSE TTE GRB pulse catalog (Hakkila+, 2018)J/ApJ/880/76 : 6 GRBs with Swift XRT and Fermi GBM obs. (Ahlgren+, 2019)J/ApJ/886/20 : Bayesian time-resolved spectra of Fermi GBM pulses (Yu+, 2019)J/ApJ/893/46 : The 4th Fermi-GBM GRB catalog: 10 years (von Kienlin+, 2020)http://heasarc.gsfc.nasa.gov/W3Browse/fermi/fermigbrst.html : Fermi GBM cat.https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJS/254/35https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/ApJS/254/35http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/ApJS/254/35https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/ApJS/254/35https://vizier.iucaa.in/viz-bin/votable?-source=J/ApJS/254/35http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/ApJS/254/35GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/ApJS/254/35/table1 (Global properties of the sample)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/ApJS/254/35/table1?https://vizier.iucaa.in/viz-bin/conesearch/J/ApJS/254/35/table1?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/ApJS/254/35/table1?GETtext/xml+votable180.050000true250.518.40.0055555555555555566/1685 2414 3602 3881 4902 5260 6214 6802 7332 8976 9060 9662 14065 15553 16282 20077 20885 22299 23576 28342 28959 30051 30595 32419 33240 33583 34933 35533 36107 36460 37391 37505 37896 39491 42710 44617 47856 48171 49089https://cdsarc.cds.unistra.fr/viz-bin/moc/J/ApJS/254/35?format=asciiGamma-raydefaultJ/ApJS/254/35/table1Global properties of the samplerecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintFermiFermi burst ID (<Fermi bnYYMMDDddd> in Simbad)meta.id;meta.mainlongprimaryz[0.42/2.73]? Redshiftsrc.redshiftfloatnullableT90[3/449.5] Observed duration t_90_stime.durationfloate_T90[0.09/6.7] T90 uncertaintysstat.errorfloatDetUsed detectors (1)meta.id;instrchardelTSelected source intervaltime.intervalchardelTbkgBackground intervalstime.intervalcharTot[15/110] Number of total BBlock time bins (S>=20)meta.numberintEff[9/81] Number of used BBlock time bins (S>=20)meta.numberintNtot[2/6] Number of total pulses for each burstmeta.numberintNuse[2/5] Number of used pulses for each burstmeta.numberintNspNumber of time-resolved spectral fit measurements for this GRB (link to table B1)meta.numberintnullableGBM4Display data from the 4th Fermi GBM cat. (von Kienlin+, 2020, J/ApJ/893/46) if anymeta.ref.urlcharSimbadSimbad column added by the CDSmeta.ref.urlchar_RARight Ascension (J2000) from SIMBAD (not part of the original data)degpos.eq.ra;meta.main_DEDeclination (J2000) from SIMBAD (not part of the original data)degpos.eq.dec;meta.mainJ/ApJS/254/35/tableb1*Time-resolved spectral fitsrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintFermiFermi burst ID (1)meta.id;meta.mainlongPulse[1/6] Pulse numbermeta.idintBinStart[-0.92/215.1] Start time of the BBlocks time binstime.epochfloatBinStop[0.17/216] Stop time of the BBlocks time binstime.epochfloatS[15.5/261.5] Significance S of the time binstat.fit.goodnessfloatCPL-K[0/6.15] CPL model: normalisationstat.paramfloate_CPL-K[0/0.96] Upper uncertainty on CPL-Kstat.errorfloatE_CPL-K[0/0.9] Lower uncertainty on CPL-Kstat.error;stat.maxfloatCPL-a[-1.97/0.76] CPL model: low-energy power-law index {alpha}spect.indexfloate_CPL-a[0.01/0.33] Upper uncertainty on CPL-astat.errorfloatE_CPL-a[0.01/0.33] Lower uncertainty on CPL-astat.error;stat.maxfloatCPL-Ec[11/4977] CPL model: cut-off energykeVem.energyinte_CPL-Ec[1/2426] Upper uncertainty on CPL-EckeVstat.errorintE_CPL-Ec[1/2353] Lower uncertainty on CPL-EckeVstat.error;stat.maxintCPL-Ep[3/4891] CPL model: peak energy (2)keVem.energyinte_CPL-Ep[1/1736] Upper uncertainty on CPL-EpkeVstat.errorintE_CPL-Ep[1/1711] Lower uncertainty on CPL-EpkeVstat.error;stat.maxintCPL-nuFnu[0.24/195.6] CPL model: ({nu}Fnu) energy flux, in erg/s/cm2 units1e-06mW.m**-2em.energyfloate_CPL-nuFnu[0.03/36.1] Upper uncertainty on CPL-nuFnu1e-06mW.m**-2stat.errorfloatE_CPL-nuFnu[0.02/22.5] Lower uncertainty on CPL-nuFnu1e-06mW.m**-2stat.error;stat.maxfloatBand-K[0/6.52] Band model: normalisationstat.paramfloate_Band-K[-0.01/1.95] Upper uncertainty on Band-Kstat.errorfloatE_Band-K[0/1.74] Lower uncertainty on Band-Kstat.error;stat.maxfloatBand-a[-1.94/0.93] Band model: low-energy power-law index {alpha}spect.indexfloate_Band-a[-0/0.58] Upper uncertainty on Band-astat.errorfloatE_Band-a[0.01/0.75] Lower uncertainty on Band-astat.error;stat.maxfloatBand-B[-7.7/-1.86] Band model: high-energy power-law index {Beta}spect.indexfloate_Band-B[0.01/3.2] Upper uncertainty on Band-Bstat.errorfloatE_Band-B[0.01/3.5] Lower uncertainty on Band-Bstat.error;stat.maxfloatBand-Ep[9/4582] Band model: peak energy EpkeVem.energyfloate_Band-Ep[0.3/1167] Upper uncertainty on Band-EpkeVstat.errorfloatE_Band-Ep[0.3/1044] Lower uncertainty on Band-EpkeVstat.error;stat.maxfloatBand-nuFnu[0.29/232.7] Band model: ({nu}Fnu) energy flux, in erg/s/cm2 units1e-06mW.m**-2em.energyfloate_Band-nuFnu[0.04/42] Upper uncertainty on Band-nuFnu1e-06mW.m**-2stat.errorfloatE_Band-nuFnu[0.04/22] Lower uncertainty on Band-nuFnu1e-06mW.m**-2stat.error;stat.maxfloatdDIC[-261/24.3] Difference, DIC_Band_-DIC_CPL_ (3)stat.paramfloatpCPL[-57/3.1] Effective number of parameters (p_DIC_) for the CPLstat.paramfloatpBand[-57.8/4] Effective number of parameters (p_DIC_) for the Band modelstat.paramfloat