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<ri:Resource created="2022-02-10T08:21:30Z" status="active" updated="2025-05-19T09:50:00Z" version="1.2" xmlns:cs="http://www.ivoa.net/xml/ConeSearch/v1.0" xmlns:ri="http://www.ivoa.net/xml/RegistryInterface/v1.0" xmlns:vr="http://www.ivoa.net/xml/VOResource/v1.0" xmlns:vs="http://www.ivoa.net/xml/VODataService/v1.1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.ivoa.net/xml/ConeSearch/v1.0 http://vo.ari.uni-heidelberg.de/docs/schemata/ConeSearch.xsd http://www.ivoa.net/xml/VOResource/v1.0 http://vo.ari.uni-heidelberg.de/docs/schemata/VOResource.xsd http://www.ivoa.net/xml/VODataService/v1.1 http://vo.ari.uni-heidelberg.de/docs/schemata/VODataService.xsd" xsi:type="vs:CatalogService"><title>1366 LGRB redshifts estimates with BARSE</title><shortName>J/ApJ/903/33</shortName><identifier>ivo://CDS.VizieR/J/ApJ/903/33</identifier><altIdentifier>doi:10.26093/cds/vizier.19030033</altIdentifier><curation><publisher ivo-id="ivo://CDS">CDS</publisher><creator><name>Osborne</name></creator><creator><name>Osborne J.A.</name></creator><creator><name>Shahmoradi A.</name></creator><creator><name>Nemiroff R.J.</name></creator><date role="Updated">2022-03-14T07:41:06Z</date><date role="Created">2022-02-10T08:21:30Z</date><contact><name>CDS support team</name><address>CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France</address><email>cds-question@unistra.fr</email></contact></curation><content><subject>gamma-ray-astronomy</subject><subject>gamma-ray-bursts</subject><subject>redshifted</subject><description>We present a catalog of probabilistic redshift estimates for 1366 individual Long-duration Gamma-ray Bursts (LGRBs) detected by the Burst And Transient Source Experiment (BATSE). This result is based on a careful selection and modeling of the population distribution of 1366 BATSE LGRBs in the five-dimensional space of redshift and the four intrinsic prompt gamma-ray emission properties: the isotropic 1024ms peak luminosity (L_iso_), the total isotropic emission (E_iso_), the spectral peak energy (E_pz_), as well as the intrinsic duration (T_90z_), while carefully taking into account the effects of sample incompleteness and the LGRB-detection mechanism of BATSE. Two fundamental plausible assumptions underlie our purely probabilistic approach: (1) LGRBs trace, either exactly or closely, the cosmic star formation rate, with a possibility of the excess rates of LGRBs in the nearby universe, and (2) the joint four-dimensional distribution of the aforementioned prompt gamma-ray emission properties is well described by a multivariate log-normal distribution. Our modeling approach enables us to constrain the redshifts of individual BATSE LGRBs to within 0.36 and 0.96 average uncertainty ranges at 50% and 90% confidence levels, respectively. Our redshift predictions are completely at odds with the previous redshift estimates of BATSE LGRBs that were computed via the proposed phenomenological high-energy relations, specifically, the apparently strong correlation of LGRBs' peak luminosity with the spectral peak energy, lightcurve variability, and spectral lag. The observed discrepancies between our predictions and the previous works can be explained by the strong influence of detector threshold and sample incompleteness in shaping these phenomenologically proposed high-energy correlations in the literature. Finally, we also discuss the potential effects of an excess cosmic rate of LGRBs at low redshifts and the possibility of a luminosity evolution of LGRBs on our results.</description><source format="bibcode">2020ApJ...903...33O</source><referenceURL>https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/903/33</referenceURL><type>Catalog</type><contentLevel>Research</contentLevel><relationship><relationshipType>IsServedBy</relationshipType><relatedResource ivo-id="ivo://CDS.VizieR/TAP">TAP VizieR generic service</relatedResource></relationship><relationship><relationshipType>IsServedBy</relationshipType><relatedResource>Conesearch service</relatedResource></relationship><relationship><relationshipType>related-to</relationshipType><relatedResource ivo-id="ivo://CDS.VizieR/IX/20">IX/20 : The Fourth BATSE Burst Revised Catalog (Paciesas+ 1999)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/ApJ/763/15">J/ApJ/763/15 : Fermi GRB analysis. III. T_90_ distributions (Qin+, 2013)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/ApJS/208/21">J/ApJS/208/21 : The BATSE 5B GRB spectral catalog (Goldstein+, 2013)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/ApJ/829/7">J/ApJ/829/7 : 3rd Swift/BAT GRB catalog (past ~11yrs) (BAT3) (Lien+, 2016)</relatedResource><relatedResource ivo-id="ivo://CDS.VizieR/J/ApJ/850/161">J/ApJ/850/161 : Konus-Wind cat. of GRBs with redshifts. I. 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Should Not be used for identification.</description><ucd>meta.record</ucd><dataType xsi:type="vs:VOTableType">int</dataType></column><column><name>ID</name><description>[105/8121] BATSE trigger identifier</description><ucd>meta.id;meta.main</ucd><dataType xsi:type="vs:VOTableType">int</dataType></column><column><name>H06</name><description>[0.22/3.94] Hopkins &amp; Beacom, 2006ApJ...651..142H model mean redshift</description><ucd>src.redshift.phot</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>b_H06</name><description>[0.1/2.4] Lower 90% prediction interval boundary in H06</description><ucd>src.redshift.phot;stat.min</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>B_H06</name><description>[0.4/5.5] Upper 90% prediction interval boundary in H06</description><ucd>src.redshift.phot;stat.max</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>B10</name><description>[0.27/8.57] Butler+, 2010, J/ApJ/711/495 model mean redshift</description><ucd>src.redshift.phot</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>b_B10</name><description>[0.1/6.7] Lower 90% prediction interval boundary in B10</description><ucd>src.redshift.phot;stat.min</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>B_B10</name><description>[0.4/11] Upper 90% prediction interval boundary in B10</description><ucd>src.redshift.phot;stat.max</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>M17</name><description>[0.2/5.31] Madau &amp; Fragos, 2017ApJ...840...39M model mean redshift</description><ucd>src.redshift.phot</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>b_M17</name><description>[0.1/4.3] Lower 90% prediction interval boundary in M17</description><ucd>src.redshift.phot;stat.min</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>B_M17</name><description>[0.2/6.4] Upper 90% prediction interval boundary in M17</description><ucd>src.redshift.phot;stat.max</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>P15</name><description>[0.17/4.57] Petrosian+, 2015ApJ...806...44P model mean redshift</description><ucd>src.redshift.phot</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>b_P15</name><description>[0.1/3.7] Lower 90% prediction interval boundary in P15</description><ucd>src.redshift.phot;stat.min</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>B_P15</name><description>[0.2/5.4] Upper 90% prediction interval boundary in P15</description><ucd>src.redshift.phot;stat.max</ucd><dataType xsi:type="vs:VOTableType">float</dataType></column><column><name>Simbad</name><description>Simbad column added by the CDS</description><ucd>meta.ref.url</ucd><dataType xsi:type="vs:VOTableType" arraysize="*">char</dataType></column><column><name>_RA</name><description>Position from SIMBAD (right ascension part)</description><unit>deg</unit><ucd>pos.eq.ra;meta.main</ucd><flag>nullable</flag></column><column><name>_DE</name><description>Position from SIMBAD (declination part)</description><unit>deg</unit><ucd>pos.eq.dec;meta.main</ucd><flag>nullable</flag></column></table></schema></tableset></ri:Resource>