The formation and early evolution of embedded star clusters in spiral galaxies
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We present Ekster, a new method for simulating star clusters from birth in a live galaxy simulation that combines the smoothed-particle hydrodynamics (SPH) method Phantom with the N-body method PeTar. With Ekster, it becomes possible to simulate individual stars in a simulation with only moderately high resolution for the gas, allowing us to study whole sections of a galaxy rather than be restricted to individual clouds. We use this method to simulate star and star cluster formation in spiral arms, investigating massive GMCs and spiral arm regions with lower mass clouds, from two galaxy models with different spiral potentials. After selecting these regions from pre-run galaxy simulations, we re-sample the particles to obtain a higher resolution. We then re-simulate these regions for 3 Myr to study where and how star clusters form. We analyse the early evolution of the embedded star clusters in these regions. We find that the massive GMC regions, which are more common with stronger spiral arms, form more massive clusters than the sections of spiral arms containing lower mass clouds. Clusters form both by accreting gas and by merging with other proto-clusters, the latter happening more frequently in the denser GMC regions.
Rieder , S , Dobbs , C , Bending , T , Liow , K Y & Wurster , J 2022 , ' The formation and early evolution of embedded star clusters in spiral galaxies ' , Monthly Notices of the Royal Astronomical Society , vol. 509 , no. 4 , stab3425 , pp. 6155-6168 . https://doi.org/10.1093/mnras/stab3425
Monthly Notices of the Royal Astronomical Society
Copyright © 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the final published version of the work, which was originally published at https://doi.org/10.1093/mnras/stab3425.
DescriptionThe equipment was funded by BEIS capital funding via STFC capital grants ST/K000373/1 and ST/R002363/1 and STFC DiRAC Operations grant ST/K001014/1. DiRAC is part of the National E-Infrastructure. SR acknowledges funding from STFC Consolidated Grant ST/R000395/1. CLD acknowledges funding from the European Research Council for the Horizon 2020 ERC consolidator grant project ICYBOB, grant number 818940.
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