04444nam a22005175i 4500 978-981-97-8735-7 DE-He213 20251030114642.0 cr nn 008mamaa 241226s2024 si | s |||| 0|eng d 9789819787357 978-981-97-8735-7 10.1007/978-981-97-8735-7 doi QB4-4.9 PGG bicssc SCI004000 bisacsh PGG thema Omori, Kiyoaki Christopher. author. aut http://id.loc.gov/vocabulary/relators/aut 89649 Galaxy Merger Identification Methods, and Investigations of the Role of Mergers in Galaxy Evolution [electronic resource] / by Kiyoaki Christopher Omori. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XVI, 144 p. 75 illus., 71 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Theses, Recognizing Outstanding Ph.D. Research, 2190-5061 -- 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. This 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. Astronomy Observations. 67976 Astrophysics. 68037 Astronomy, Observations and Techniques. 67977 Astrophysics. 68037 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819787340 Printed edition: 9789819787364 Printed edition: 9789819787371 Springer Theses, Recognizing Outstanding Ph.D. Research, 2190-5061 66370 https://doi.org/10.1007/978-981-97-8735-7 ZDB-2-PHA ZDB-2-SXP 66079 66079