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020 _a9789819787357
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024 7 _a10.1007/978-981-97-8735-7
_2doi
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100 1 _aOmori, Kiyoaki Christopher.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_989649
245 1 0 _aGalaxy Merger Identification Methods, and Investigations of the Role of Mergers in Galaxy Evolution
_h[electronic resource] /
_cby Kiyoaki Christopher Omori.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXVI, 144 p. 75 illus., 71 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5061
505 0 _a -- 1 Introduction -- 2 The Spatially Resolved Mass Metallicity Relation in Mergers -- 3 The effect of environment in galaxy merger incidence -- 4 The impact of galaxy mergers on AGN activity -- 5 Conclusions -- 6 Appendices.
520 _aThis book offers new findings on the impact of galaxy mergers on galaxy evolution and related processes. Galaxies are astronomical objects composed of stars, gas, and dark matter. The main pathway for galaxy evolution in the currently accepted model for structure growth is through interactions and mergers, or the process in which multiple galaxies interact, collide, and end up as one larger galaxy. Galaxy interactions and mergers are also considered to drive various processes pertaining to galaxy evolution, such as star formation, active galactic nuclei (AGN) activity, and chemical evolution. As such, not only are merging galaxy systems important to understand galaxy structure growth in a cosmological context, they are also unique laboratories to study evolutionary processes taking place in galaxies. However, despite their importance, the relative role of galaxy mergers on galaxy evolution is still heavily contested. One reason for this is difficulty in identifying merger galaxies in observational data. Many different methods have been adopted for merger galaxy identification, but none of them are perfect. Moreover, the use of differing selection methods can result in widely different samples, which can alter statistical results. As such, a novel, robust method for merger identification is required to deepen our understanding of galaxy mergers. In this book, we introduce new methods to identify galaxy mergers from observational data. The first method combines the use of visual identification—both of optical and kinematic images—with spectroscopic pair identification. The second is machine learning based, using a training method called fine-tuning, an application of transfer learning. A pre-trained model on galaxy images is further fine-tuned using a sample of synthetic galaxy images, to make a classifier for merger identification. Using samples identified by our methods, the author conducts three studies: the impact of mergers on galaxy chemical evolution, the effect of galaxy local environment on merging activity, and the connection between mergers and AGN activity. This book is mainly intended for astrophysicists, particularly those studying galaxies, however is open for all to read.
650 0 _aAstronomy
_xObservations.
_967976
650 0 _aAstrophysics.
_968037
650 1 4 _aAstronomy, Observations and Techniques.
_967977
650 2 4 _aAstrophysics.
_968037
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819787340
776 0 8 _iPrinted edition:
_z9789819787364
776 0 8 _iPrinted edition:
_z9789819787371
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5061
_966370
856 4 0 _uhttps://doi.org/10.1007/978-981-97-8735-7
912 _aZDB-2-PHA
912 _aZDB-2-SXP
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