J/MNRAS/504/3084ivo://CDS.VizieR/J/MNRAS/504/3084doi:10.26093/cds/vizier.75043084CDSAbraham S.Mukund N.Vibhute A.Sharma V.Iyyani S.Bhattacharya D.,Rao A.R.Vadawale S. and Bhalerao V.2024-11-06T20:33:39Z2024-05-27T15:06:14ZCDS support team

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

cds-question@unistra.frgamma-ray-astronomygamma-ray-burstsx-ray-sourcesWe present a machine learning (ML) based method for automated detection of Gamma-Ray Burst (GRB) candidate events in the range 60-250 keV from the AstroSat Cadmium Zinc Telluride Imager data. We use density-based spatial clustering to detect excess power and carry out an unsupervised hierarchical clustering across all such events to identify the different light curves present in the data. This representation helps us to understand the instrument's sensitivity to the various GRB populations and identify the major non-astrophysical noise artefacts present in the data. We use Dynamic Time Warping (DTW) to carry out template matching, which ensures the morphological similarity of the detected events with known typical GRB light curves. DTW alleviates the need for a dense template repository often required in matched filtering like searches. The use of a similarity metric facilitates outlier detection suitable for capturing previously unmodelled events. We briefly discuss the characteristics of 35 long GRB candidates detected using the pipeline and show that with minor modifications such as adaptive binning, the method is also sensitive to short GRB events. Augmenting the existing data analysis pipeline with such ML capabilities alleviates the need for extensive manual inspection, enabling quicker response to alerts received from other observatories such as the gravitational-wave detectors.2021MNRAS.504.3084Ahttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/504/3084CatalogResearchIsServedByTAP VizieR generic servicerelated-toJ/ApJ/888/40 : Fast radio bursts with AstroSat/CZTI (Anumarlapudi+, 2020)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/MNRAS/504/3084https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/MNRAS/504/3084http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/MNRAS/504/3084https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/MNRAS/504/3084https://vizier.iucaa.in/viz-bin/votable?-source=J/MNRAS/504/3084http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/MNRAS/504/3084GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapGamma-rayX-raydefaultJ/MNRAS/504/3084/table1GRBs candidate events detected with the machine learning algorithm described in this paper, GCN circulars have been issued for the highlighted eventsrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintIDGamma ray burst GRB ID (GRB ID)meta.id;meta.maincharObs.DateUT Date of observation (UTC date)time.epoch;obsnullableObs.TimeUT time of exposure start (UTC time)time.start;obs.exposurenullableT90GRB event duration time (T90) (1)stime.durationfloate_T90Mean error on T90 (e_T90)sstat.errorfloatPeakCount rate peak (Peak)ct/sphot.count;em.gamma.hardinte_PeakMean error on Peak (e_Peak)ct/sstat.error;stat.maxfloatTotalTotal of counts (Total counts)ctphot.count;em.gamma.hardinte_TotalMean error on Total (e_Total)ctstat.errorfloatMeanBgMean of the background count rate (Mean background) (2)ct/sinstr.backgroundinte_MeanBgMean error of MeanBg (e_MeanBg)ct/sstat.errorfloatSigniDetection significance (Signi) (3)stat.fit.goodness;instr.saturationfloate_SigniMean error (standard deviation) on Signi (e_Signi) (3)stat.errorfloat