부산대학교 도서관

Galaxy mergers play a crucial role in galaxy evolution. However, the correlation between mergers and the local environment of galaxies is not fully understood. We aim to address the question of whether galaxy mergers prefer denser or less dense environments by quantifying the spatial clustering of mergers and non-mergers. We use two different indicators to classify mergers and non-mergers - classification based on a deep learning technique ($f$) and non-parametric measures of galaxy morphology, Gini-$M_{20}$ ($g$). We used a set of galaxy samples in the redshift range 0.1 < z < 0.15 from the Galaxy and Mass Assembly (GAMA) survey with a stellar mass cut of log (M*/Msun) > 9.5. We measured and compared the two-point correlation function (2pCF) of mergers and non-mergers classified using the two merger indicators $f$ and $g$. We measured the marked correlation function (MCF), in which the galaxies are weighted by $f$ to probe the environmental dependence of galaxy mergers. We do not observe a statistically significant difference between the clustering strengths of mergers and non-mergers obtained using 2pCF. However, using the MCF measurements with $f$ as a mark, we observe an anti-correlation between the likelihood of a galaxy being a merger and its environment. Our results emphasise the advantage of MCF over 2pCF in probing the environmental correlations. Based on the MCF measurements, we conclude that the galaxy mergers prefer to occur in the under-dense environments on scales greater than 50 kpc/h of the large-scale structure (LSS). We attribute this observation to the high relative velocities of galaxies in the densest environments that prevent them from merging.
Comment: 13 pages, 9 figures, accepted for publication in A&A