We introduce a novel halo/galaxy matching technique between two cosmological simulations with different resolutions, which utilizes the positions and masses of halos along their subhalo merger tree. With this tool, we conduct a study of resolution biases through the {\it galaxy-by-galaxy} inspection of a pair of simulations that have the same simulation configuration but different mass resolutions, utilizing a suite of {\sc IllustrisTNG} simulations to assess the impact on galaxy properties. We find that, with the subgrid physics model calibrated for TNG100-1, subhalos in TNG100-1 (high resolution) have $\lesssim0.5$ dex higher stellar masses than their counterparts in the TNG100-2 (low-resolution). It is also discovered that the subhalos with $M_{\mathrm{gas}}\sim10^{8.5}\,{\rm M}_\odot$ in TNG100-1 have $\sim0.5$ dex higher gas mass than those in TNG100-2. The mass profiles of the subhalos reveal that the dark matter masses of subhalos in TNG100-2 converge well with those from TNG100-1, except within 4 kpc of the resolution limit. The differences in stellar mass and hot gas mass are most pronounced in the central region. We exploit machine learning to build a correction mapping for the physical quantities of subhalos from low- to high-resolution simulations (TNG300-1 and TNG100-1), which enables us to find an efficient way to compile a high-resolution galaxy catalog even from a low-resolution simulation. Our tools can easily be applied to other large cosmological simulations, testing and mitigating the resolution biases of their numerical codes and subgrid physics models.
Observationally derived change in the star formation rate as mergers progress
National Centre for Nuclear Research（国家核研究中心）；SRON Netherlands Institute for Space Research（荷兰空间研究研究所）；Kapteyn Astronomical Institute, University of Groningen（格罗宁根大学卡普坦天文研究所）；Instituto de Radioastronomía y Astrofísica, Universidad Nacional Autónoma de México（墨西哥国立自治大学射电天文学与天体物理研究所）；Tartu Observatory, University of Tartu（塔尔图大学塔尔图天文台）
The AGORA High-resolution Galaxy Simulations Comparison Project. IX - Part 1: Effects of a Major Galaxy Merger on Star Formation of a Milky Way-mass Galaxy Progenitor
National Center for Supercomputing Applications（国家超级计算应用中心）；Seoul National University（首尔国立大学）；Universidad Complutense de Madrid（马德里康普顿斯大学）；Lund University（隆德大学）
The AGORA High-resolution Galaxy Simulations Comparison Project. IX - Part 2: Effects of a Major Galaxy Merger on the Stellar Morphology of a Milky Way-mass Galaxy Progenitor
Center for AstroPhysical Surveys, National Center for Supercomputing Applications（国家超级计算应用中心天文物理调查中心）；Center for Theoretical Physics, Department of Physics and Astronomy, Seoul National University（首尔国立大学物理与天文学系理论物理中心）；Departamento de Física de la Tierra y Astrofísica, Fac. de C.C. Físicas, Universidad Complutense de Madrid（马德里康普顿斯大学理学院地球与天体物理学系）；Instituto de Física de Partículas y del Cosmos, IPARCOS, Fac. C.C. Físicas, Universidad Complutense de Madrid（马德里康普顿斯大学理学院粒子与宇宙物理研究所）；Lund Observatory, Division of Astrophysics, Department of Physics, Lund University（隆德大学物理系天体物理学部隆德天文台）
Preliminary census of galaxies in the LISA localisation volume: I. Searching for LISA candidate massive black hole binary merger hosts using Sloan Digital Sky Survey photometry
Merging Cluster Collaboration: A Panchromatic Atlas of Radio Relic Mergers
Golovich et al. 2017 b展示了对29个射电遗迹合并星系团的光学成像和光谱调查。在本文中，我们研究这项调查，以确定子结构和量化的动态合并。使用相结合的光度和光谱的方法，我们确定在每个系统中的子结构的最小数量来描述星系的人口和估计的视线之间的速度差异可能合并的子集群。我们发现，合并的视线速度分量通常是小的相比，最大的三维相对速度（通常$<1000$公里的$^{-1}$，往往与零一致）。这表明，这些系统的合并轴通常在或接近天空的平面，与磁流体动力学模拟的结果相匹配。在29个系统中的28个系统中，我们确定了与射电遗迹对齐的星系群中的子结构，并推测了相关的合并引起的冲击。从这个合奏，我们确定了八个系统，包括在一个“黄金”样本，这是进一步观察，建模和模拟研究的主要。额外的论文将呈现弱透镜质量图和每个合并系统的动力学建模，最终导致对宇宙中一些最大尺度的广泛天体物理现象的新见解。
A Deep Learning Approach for Characterizing Major Galaxy Mergers
LERMA, Observatoire de Paris, PSL Research University, CNRS Sorbonne Universités, UPMC Univ. Paris 06（巴黎天文台LERMA实验室，巴黎文理研究大学，法国国家科学研究中心索邦大学，巴黎第六大学）；Departamento de Astrofísica, Universidad de La Laguna（拉古纳大学天体物理系）；Institut d’Astrophysique de Paris, Sorbonne Université CNRS（索邦大学巴黎天体物理研究所，法国国家科学研究中心）；Artificial Intelligence Research Institute (IIIA-CSIC)（人工智能研究所（IIIA-CSIC））；UCO/Lick Observatory, Department of Astronomy and Astrophysics University of California, Santa Cruz（加州大学圣克鲁兹分校UCO/利克天文台天文与天体物理系）；DeepMind（深度思维）；Instituto de Astrofísica de Canarias（加那利群岛天体物理研究所）；IFCA Instituto de Fiésica de Cantabria (UC-CSIC)（坎塔布里亚物理研究所（UC-CSIC））
