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The Galaxy Replacement Technique (GRT): A New Approach to Study Tidal Stripping and Formation of Intracluster Light in a Cosmological Context

Chun et al. (2022)

Kyungwon Chun #1 Lead Jihye Shin #2 Co-author Jongwan Ko #4 Co-author Jaewon Yoo #5 Co-author
THE ASTROPHYSICAL JOURNAL IF 5.877 A 등급

Abstract

We introduce the Galaxy Replacement Technique (GRT) that allows us to model tidal stripping of galaxies with very high mass (mstar = 5.4 × 104 Me h-1) and high spatial resolution (10 pc h-1), in a fully cosmological context, using an efficient and fast technique. The technique works by replacing multiple low-resolution dark-matter (DM) halos in the base cosmological simulation with high-resolution models, including a DM halo and stellar disk. We apply the method to follow the hierarchical buildup of a cluster since redshift ∼8 to now, through the hierarchical accretion of galaxies, individually or in substructures such as galaxy groups. We find we can successfully reproduce the observed total stellar masses of observed clusters since redshift ∼1. The high resolution allows us to accurately resolve the tidal stripping process and well describe the formation of ultralow surface brightness features in the cluster (μV < 32 mag arcsec-2) such as the intracluster light (ICL), shells, and tidal streams. We measure the evolution of the fraction of light in the ICL and brightest cluster galaxy using several different methods. While their broad response to the cluster-mass growth history is similar, the methods show systematic differences, meaning we must be careful when comparing studies that use distinct methods. The GRT represents a powerful new tool for studying tidal effects on galaxies and exploring the formation channels of the ICL in a fully cosmological context and with large samples of simulated groups and clusters.

AI Classification

Maturity Tier

Tier 3 — Physics-tailored

AI Role

simulation / emulation

core

Techniques

other

Tasks

generative/simulation-emulation

Subfield

galaxies

simulation

Flags

Develops AI method
· Classification confidence: 90%