Massive star clusters in the semi-analytical galaxy formation model <i>L-Galaxies</i> 2020

It has been established that a direct link exists between the formation history of star cluster populations and their host galaxies. However, our lack of understanding of the assembly of star cluster populations impedes our ability to use them as tracers of galaxy evolution. In this work we introduce a new variation of the L-Galaxies 2020 semi-analytic galaxy formation model that includes the formation of star clusters above 10 4 M ⊙ and probes different physical assumptions that affect their evolution over cosmic time. We used properties of different galaxy components and localised star formation to determine the bound fraction of star formation in disks. After randomly sampling masses from an environmentally dependent star cluster initial mass function, we assigned to each object a half-mass radius, metallicity, and distance from the galaxy centre. We considered up to 2000 individual star clusters per galaxy and evolved their properties over time while taking into account stellar evolution, two-body relaxation, tidal shocks, dynamical friction, and re-positioning during galaxy mergers. Our simulation successfully reproduces several observational quantities, such as the empirical relationship between the absolute V -band magnitude of the brightest young star clusters and the host galaxy star formation rate, the mass function of young star clusters, and the mean metallicities of the star cluster distributions versus galaxy masses. The simulation reveals great complexity in the z = 0 star cluster population resulting from differential destruction channels and origins, including in situ populations in the disk, a major merger-induced heated component in the halo, and accreted star clusters. Model variations point out the importance of the shape of the star cluster initial mass function, the initial distribution of half-mass radii, and the relationship between the sound speed of cold gas and the star formation rate. Our new model provides new avenues to trace individual star clusters and test cluster-related physics within a cosmological set-up in a computationally efficient manner.

Authors

Institutions

Publication Details

Journal
Astronomy and Astrophysics
Published
2026-05-01
DOI
https://doi.org/10.1051/0004-6361/202554325
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Massive star clusters in the semi-analytical galaxy formation model L-Galaxies 2020

M. Polkas, Silvia Bonoli, Nils Hoyer, M. Celeste Artale et al.
Astronomy and Astrophysics
Galaxies: Formation, Evolution, Phenomena
article

Massive star clusters in the semi-analytical galaxy formation model L-Galaxies 2020

M. Polkas, Silvia Bonoli, Nils Hoyer, M. Celeste Artale, Robert M. Yates, Daniele Spinoso, N. Bastian, Diego Herrero-Carrión, David Izquierdo-Villalba, Nadine Neumayer
article en

Abstract

It has been established that a direct link exists between the formation history of star cluster populations and their host galaxies. However, our lack of understanding of the assembly of star cluster populations impedes our ability to use them as tracers of galaxy evolution. In this work we introduce a new variation of the L-Galaxies 2020 semi-analytic galaxy formation model that includes the formation of star clusters above 10 4 M ⊙ and probes different physical assumptions that affect their evolution over cosmic time. We used properties of different galaxy components and localised star formation to determine the bound fraction of star formation in disks. After randomly sampling masses from an environmentally dependent star cluster initial mass function, we assigned to each object a half-mass radius, metallicity, and distance from the galaxy centre. We considered up to 2000 individual star clusters per galaxy and evolved their properties over time while taking into account stellar evolution, two-body relaxation, tidal shocks, dynamical friction, and re-positioning during galaxy mergers. Our simulation successfully reproduces several observational quantities, such as the empirical relationship between the absolute V -band magnitude of the brightest young star clusters and the host galaxy star formation rate, the mass function of young star clusters, and the mean metallicities of the star cluster distributions versus galaxy masses. The simulation reveals great complexity in the z = 0 star cluster population resulting from differential destruction channels and origins, including in situ populations in the disk, a major merger-induced heated component in the halo, and accreted star clusters. Model variations point out the importance of the shape of the star cluster initial mass function, the initial distribution of half-mass radii, and the relationship between the sound speed of cold gas and the star formation rate. Our new model provides new avenues to trace individual star clusters and test cluster-related physics within a cosmological set-up in a computationally efficient manner.

Astronomy and AstrophysicsVol. 709
Ikerbasque (ES), Centre National de la Recherche Scientifique (FR), Institute of Space Sciences (ES), University of Hertfordshire (GB), University of the Basque Country (ES), Université Paris Cité (FR), Heidelberg University (DE), Sorbonne Université (FR), Sorbonne Paris Cité (FR), Max Planck Institute for Astronomy (DE), Institute of Astronomy (RU), Astronomy and Space (AU), Universidad de Ciencias Médicas de la Habana (CU), Donostia International Physics Center (ES), University of Milano-Bicocca (IT)
Openalex Percentile: Top 6%
Galaxies: Formation, Evolution, Phenomena
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.