collaborators

13 papers

astro-ph.GA2026

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

Thinh Huu Nguyen, Kirk S. S. Barrow, Minyong Jung +23

Galaxy mergers, with their high sensitivity to initial conditions, provide a valuable setting for comparative studies of galaxy simulation codes. Following our first paper focusing…

astro-ph.GA2026

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

Thinh Huu Nguyen, Kirk S. S. Barrow, Minyong Jung +23

Given their highly nonlinear dynamics and sensitivity to initial conditions, galaxy mergers are a compelling area to conduct a simulation code comparison. We perform a comparative…

astro-ph.CO2026

Here, There and Everywhere: How AGN jets affect galaxy cluster environments

Isaac Rosenberg, Martine Lokken, Renée Hložek +3

The paper uses zoom‑in hydrodynamic simulations of galaxy clusters to test how AGN jet properties (velocity, orientation, coupling) affect the thermal state of surrounding gas and…

astro-ph.GA2026

A Consistent Comparison of Intracluster Light Assembly in Simulations I. Redshift Evolution and Progenitor Galaxies

Harley J. Brown, Garreth Martin, Frazer R. Pearce +5

The tidal stripping of satellite galaxies and the stellar detritus ejected during galaxy mergers builds up a diffuse stellar component in galaxy clusters known as the intracluster…

astro-ph.GA2026

Anisotropy of Satellite Galaxies-I: Contrasting Correlations with Central Galaxy, Host Halo, and Large-Scale Filament Structures

Zhuoming Zhang, Weiguang Cui, Yun Chen +2

Using the SIMBA, EAGLE, and IllustrisTNG-100 galaxy formation simulations, we examine the anisotropy of the satellite distribution and its dependencies on central galaxies, host ha…

astro-ph.GA2025

Populating Galaxies Into Halos Via Machine Learning on the Simba Simulation

Pratyush Kumar Das, Romeel Davé, Weiguang Cui

We present a machine-learning framework, Machine Inferred Galaxy (MIG), to populate dark-matter haloes with galaxies in N-body simulations. MIG predicts stellar mass (), st…