J/A+A/679/A142ivo://CDS.VizieR/J/A+A/679/A142doi:10.26093/cds/vizier/bc-n7/lbecCDSOmori K.C.Bottrell C.Walmsley M.Yesuf H.M.Goulding A.D.Ding X.,Popping G.Silverman J.D.Takeuchi T.T.Toba Y.2026-07-01T14:01:14Z2023-11-29T10:07:39ZCDS support team

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

cds-question@unistra.frgalaxiesvisible-astronomyinteracting-galaxiesredshiftedGalaxy mergers and interactions are an important process within the context of galaxy evolution, however, there is still no definitive method which identifies pure and complete merger samples is still not definitive. A method for creating such a merger sample is required so that studies can be conducted to deepen our understanding of the merger process and its impact on galaxy evolution. In this work, we take a deep learning-based approach for galaxy merger identification in Subaru HSC-SSP, specifically through the use of deep representation learning and fine-tuning, with the aim of creating a pure and complete merger sample within the HSC-SSP survey. We can use this merger sample to conduct studies on how mergers affect galaxy evolution. We use Zoobot, a deep learning representation learning model pre-trained on citizen science votes on Galaxy Zoo DeCALS images. We fine-tune Zoobot for the purpose of merger classification of images of SDSS and GAMA galaxies in HSC-SSP PDR 3. Fine-tuning is done using 1200 synthetic HSC-SSP images of galaxies from the TNG simulation. We then find merger probabilities on observed HSC images using the fine-tuned model. Using our merger probabilities, we examine the relationship between merger activity and environment. We find that our fine-tuned model returns an accuracy on the synthetic validation data of 76%. This number is comparable to those of previous studies where convolutional neural networks were trained with simulation images, but with our work requiring a far smaller number of training samples. For our synthetic data, our model is able to achieve completeness and precision values of 80%. In addition, our model is able to correctly classify both mergers and non-mergers of diverse morphologies and structures, including those at various stages and mass ratios, while distinguishing between projections and merger pairs. For the relation between galaxy mergers and environment, we find two distinct trends. Using stellar mass overdensity estimates for TNG simulations and observations using SDSS and GAMA, we find that galaxies with higher merger scores favor lower density environments on scales of 0.5 to 8h^-1^Mpc. However, below these scales in the simulations, we find that galaxies with higher merger scores favor higher density environments. We fine-tuned a citizen-science trained deep representation learning model for purpose of merger galaxy classification in HSC-SSP, and make our merger probability catalog available to the public. Using our morphology-based catalog, we find that mergers are more prevalent in lower density environments on scales of 0.5-8h^-1^Mpc.2023A&A...679A.142Ohttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/679/A142CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicehttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/679/A142https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/679/A142http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/679/A142https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/679/A142https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/679/A142http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/679/A142GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/679/A142/table3 (Merger probabilities for ~130000 HSC Galaxies)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/679/A142/table3?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/679/A142/table3?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/679/A142/table3?GETtext/xml+votable180.050000true135.2169346-0.217826050.0055555555555555564/256 1105-1107 1110 1113 1200 1365 1621-1622 1641-1642 1685-1687 1690-1691 1701-1703 1705-1708 1712 1728 5/1028 1032 2048 2050 2455 2459-2460 2471 2475-2476 2480 2496 2647 2651-2652 2672 2816 4419 4434 4449 4464 4501-4503 4508 4519 4521-4524 4693-4695 4697-4700 4709-4711 4713-4716 4718 4720 4757-4759 4764-4765 4767 4804-4806 4808-4810 4864 5459 5468-5469 5471 6399 6463 6479 6483 6492-6493 6499-6502 6504-6506 6512 6543 6548-6550 6552-6554 6563 6572 6574-6576 6735 6739 6757-6759 6768-6770 6803 6819 6836 6840 6852 6856 6916 6920 10239 11263 6/4116-4118 4120-4122 4132-4134 4136-4138 8198 8204 8206 8224-8226 9641-9643 9646 9813-9815 9817-9819 9829-9831 9833 9835 9844-9846 9848 9877-9879 9881 9883 9893-9895 9897-9899 9908-9909 9912-9913 9924 9928-9929 9988 9992-9994 10581 10583 10585-10587 10598-10599 10601-10603 10612 10614 10616-10618 10692-10694 11268 11270 11272-11274 17405-17407 17671 17675 17728 17730-17731 17740 17742-17743 17776 17778-17779 17784-17786 17793 17805 17841 17844-17845 17860-17862 17864-17866 17999 18003 18021 18036 18038 18040-18041 18070-18071 18073-18075 18082-18083 18100-18102 18104-18106 18176 18178 18184-18186 18685 18687 18749 18751 18765-18767 18769-18771 18785 18787 18804-18806 18808-18810 18829-18831 18833-18835 18849-18851 18868-18870 18876 18878-18879 18884-18886 18888-18890 18922 19022-19023 19025-19027 19044-19045 19047 19064-19065 19067 19156-19157 19228 19244-19246 19248 19250 19264 19266 19328-19329 19460-19462 19464-19466 21831 21861 21881 25591 25595 25847 25851 25911 25915 25927 25931 25976 25980-25981 25991 25995 26012-26014 26028-26030 26032-26034 26040 26052 26056 26167 26171 26183 26187 26189-26191 26204-26206 26220-26222 26224-26226 26228 26247 26251 26292 26294-26295 26308 26312 26368-26370 26376 26871 26877-26879 26935 26939 26949-26951 26955 27015 27019 27021-27023 27025-27027 27084-27086 27088 27104-27106 27108 27191 27197-27199 27211 27271 27275 27348-27350 27352-27354 27364-27366 27368-27370 27412 27414 27416-27417 27428 27432 27668 27672 27674 27684-27686 27688-27690 27692 31402 36798-36799 40951 40955 45051https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/679/A142?format=asciiOpticaldefaultJ/A+A/679/A142/table3Merger probabilities for ~130000 HSC GalaxiesrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintObjIDHSC PDR3 object ID (object_id)meta.id;meta.mainRAJ2000Right ascension (J2000) (ra)degpos.eq.ra;meta.mainindexedDEJ2000Declination (J2000) (dec)degpos.eq.dec;meta.mainindexedzSpectroscopic redshift (z)src.redshiftdoublelogM? log stellar mass (logM)log(solMass)phys.massdoublenullableReff? Effective radius (R_eff)arcsecphys.angSizedoublenullablePmergMerger probability (prediction)stat.fit.goodnessdouble