J/A+A/661/A52ivo://CDS.VizieR/J/A+A/661/A52doi:10.26093/cds/vizier.36610052CDSPearson W.J.Suelves L.E.Ho S.C.-C.Oi N.Brough S.Holwerda B.W.,Hopkins A.M.Huang T.-C.Hwang H.S.Kelvin L.S.Kim S.J.,Lopez-Sanchez A.R.MaLek K.Pearson C.Poliszczuk A.Pollo A.,Rodriguez-Gomez V.Shim H.Toba Y.Wang L.2022-11-18T09:31:43Z2022-05-17T12:18:11ZCDS support team

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

cds-question@unistra.frgalaxiescatalogsgalaxy-classification-systemsWe aim to generate a catalogue of merging galaxies within the 5.4deg^2^. North Ecliptic Pole over the redshift range 0.0<z<0.3. To do this, imaging data from the Hyper Suprime-Cam will be used along with morphological parameters derived from these same data. The catalogue is generated using a hybrid approach. Two neural networks are trained to perform binary merger non-merger classifications: one for galaxies with z<0.15 and another for 0.15<=z<0.30. Each network uses the image and morphological parameters of a galaxy as input. The galaxies that are identified as merger candidates by the network are then visually checked by experts. The resulting mergers will be used to calculate the merger fraction as a function of redshift and compared with literature results. We found that 86.3% of galaxy mergers at z<0.15 and 79.0% of mergers at 0.15<=z<0.30 are expected to be correctly identified by the networks. Of the 34264 galaxies classified by the neural networks, 10195 were found to be merger candidates. Of these, 2109 were visually identified to be merging galaxies. We find that the merger fraction increases with redshift, consistent with literature results from observations and simulations, and that there is a mild star-formation rate enhancement in the merger population of a factor of 1.102+/-0.084.2022A&A...661A..52Phttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/661/A52CatalogResearchIsServedByTAP VizieR generic servicehttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/661/A52https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/661/A52http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/661/A52https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/661/A52https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/661/A52http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/661/A52GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapOpticaldefaultJ/A+A/661/A52/table1Catalogues of optical, r-band morphologies and convolutional neural networkrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintHSCHSC IDmeta.id;meta.mainAsymmAsymmetry as described in Lotz et al. (2004AJ....128..163L, Cat. J/AJ/128/163)spect.line.asymmetrydoubleConcentConcentration as described in Lotz et al. (2004AJ....128..163L, Cat. J/AJ/128/163)meta.notedoubleDevDeviation as described in Peth et al. (2016MNRAS.458..963P, Cat. J/MNRAS/458/963)stat.stdevdoubleell-AsymmEllipticity relative to the point that minimizes the asymmetrysrc.ellipticitydoubleell-CentEllipticity relative to the centroidsrc.ellipticitydoubleelong-AsymmElongation relative to the point that minimizes the asymmetryphys.size.axisRatioelong-CentElongation relative to the centroidphys.size.axisRatioginiGini coefficient as described in Lotz et al. (2004AJ....128..163L, Cat. J/AJ/128/163)stat.fit.paramdoubleginiM20BulgeGini-M20 bulge statistic as defined in Rodriguez-Gomez et al. (2019MNRAS.483.4140R)stat.fit.paramdoubleSNperpixelsignal-to-noise per pixelstat.snrdoubleFlag[0/1] 1 if the fit is badmeta.code.errorintFlagSersic[0/1] 1 if the Sersic fit is badmeta.code.errorintginiM20MergerGini-M20 merger statistic as defined in Rodriguez-Gomez et al. (2019MNRAS.483.4140R)stat.fit.paramdoubleIntIntensity as described in Peth et al. (2016MNRAS.458..963P, Cat. J/MNRAS/458/963)spect.line.intensitydoubleM20M_20 coefficient as described in Lotz et al. (2004AJ....128..163L, Cat. J/AJ/128/163)stat.fit.paramdoubleMultimodeMultimode as described in Freeman et al. (2013MNRAS.434..282F) and Peth et al. (2016MNRAS.458..963P, Cat. J/MNRAS/458/963)stat.fit.paramdoubler20Radius that contains 20% of the lightpixphys.angSize;srcdoubler50Radius that contains 50% of the lightpixphys.angSize;srcdoubler80Radius that contains 80% of the lightpixphys.angSize;srcdoubleSersicAmpAmplitude of the 2D Sersic fit at the effective (half-light) radiussrc.var.amplitudedoubleSersicEllipEllipticity of the 2D Sersic fitsrc.ellipticitydoubleSersicnSersic index nstat.fit.paramdoubleSmoothSmoothness (a.k.a. clumpiness) as defined in Lotz et al. (2004AJ....128..163L, Cat. J/AJ/128/163)stat.fit.paramdoubleJ/A+A/661/A52/table4Visual merger classificationrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintHSCHSC IDmeta.id;meta.mainFracMergerNN merger fractionmeta.numberdoubleFracNonmergerNN non-merger fractionmeta.numberdoubleVisMerger[0/1] Visually confirmed merger (1: merger, 0: non-merger)meta.codeint