Introducing a new, robust galaxy-finder algorithm for simulations. Issue 2 (8th October 2018)

• Record Type: Journal Article
• Authors: Cañas, Rodrigo; Elahi, Pascal J; Welker, Charlotte; Lagos, Claudia del P; Power, Chris; Dubois, Yohan; Pichon, Christophe
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 482:Issue 2(2019)
• Page Start: 2039
• Page End: 2064
• Publication Date: 2018-10-08
• Subjects: methods: numerical -- galaxies: evolution -- dark matter -- cosmology: theory; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/sty2725
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: Identifying galaxies in hydrodynamical simulations is a difficult task, particularly in regions of high density such as galaxy groups and clusters. We present a new scale-free, shape-independent algorithm to robustly and accurately identify galaxies in simulations, implemented within the phase-space halo-finder codevelociraptor . This is achieved by using the full phase-space dispersion tensor for particle assignment and an iterative adjustment of search parameters, which help us overcome common structure-finding problems. We apply our improved method to the Horizon-AGN simulation and compare galaxy stellar masses ( M * ), star formation rates (SFRs), and sizes with the elaborate configuration-space halo-finderhalomaker . Galaxies living in haloes with >1 galaxy are the most affected by the shortcomings of real-space finders, with their mass, SFR, and size being >2 times larger (smaller) in the case of host (satellite) galaxies. Thus, our ability to measure minor/major merger rates and disentangle environmental effects in simulations can be generally hindered if the identification of galaxies is not treated carefully. Though large systematic differences are obtained on a one-to-one basis, the overall galaxy stellar mass function, the SFR function, and the mass–size relations are not greatly affected. This is due to isolated galaxies being the most abundant population, dominating broad statistics.