Journal article
Dark-ages Reionization and Galaxy Formation Simulation - XV. Stellar evolution and feedback in dwarf galaxies at high redshift
Yuxiang Qin, Alan R Duffy, Simon J Mutch, Gregory B Poole, Andrei Mesinger, J Stuart B Wyithe
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | OXFORD UNIV PRESS | Published : 2019
Abstract
We directly compare predictions of dwarf galaxy properties in a semi-analytic model (SAM) with those extracted from a high-resolution hydrodynamic simulation. We focus on galaxies with halo masses of 109 10), with the relevant effective timescale becoming significantly longer towards lower redshifts. This indicates efficient accretion in cold mode in these low-mass objects at high redshift. The improved SAM, which has been calibrated against hydrodynamic simulations, can provide more accurate predictions of high-redshift dwarf galaxy properties that are essential for reionization study.
Grants
Awarded by Victorian Life Sciences Computation Initiative
Awarded by Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)
Awarded by European Research Council under the European Union
Funding Acknowledgements
This research was supported by the Victorian Life Sciences Computation Initiative, grant ref. UOM0005, on its Peak Computing Facility hosted at the University of Melbourne, an initiative of the Victorian Government, Australia. Part of this work was performed on the gSTAR national facility at Swinburne University of Technology. gSTAR is funded by Swinburne and the Australian Governments Education Investment Fund. This research was conducted by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D, project #CE170100013). This work was supported by the Flagship Allocation Scheme of the NCI National Facility at the ANU, generous allocations of time through the iVEC Partner Share and Australian Supercomputer Time Allocation Committee. YQ acknowledges support from the Albert Shimmins Fund. AMacknowledges support from the European Research Council under the European Union's Horizon 2020 research and innovation program (Grant #638809-AIDA).