Sussing Merger Trees : The Impact of Halo Merger Trees on Galaxy Properties in a Semi-Analytic Model
Jaehyun Lee, Sukyoung K. Yi, Pascal J. Elahi, Peter A. Thomas, Frazer, R. Pearce, Peter Behroozi, Jiaxin Han, John Helly, Intae Jung, Alexander, Knebe, Yao-Yuan Mao, Julian Onions, Vicente Rodriguez-Gomez, Aurel Schneider,, Chaichalit Srisawat, and Dylan Tweed

TL;DR
This study examines how different methods of constructing halo merger trees influence galaxy properties in semi-analytic models, highlighting the importance of tree construction choices on model outcomes and underlying physics interpretations.
Contribution
It systematically analyzes the impact of various halo merger tree algorithms on galaxy formation predictions within semi-analytic models, emphasizing calibration effects and physical implications.
Findings
Differences in merger trees lead to variations in galaxy properties at z=0.
Calibration can reduce discrepancies in galaxy properties but affects evolutionary histories.
Underlying physics, like supernova feedback efficiency, can vary significantly depending on the merger tree used.
Abstract
A halo merger tree forms the essential backbone of a semi-analytic model for galaxy formation and evolution. Recent studies have pointed out that extracting merger trees from numerical simulations of structure formation is non-trivial; different tree building algorithms can give differing merger histories. These differences should be carefully understood before merger trees are used as input for models of galaxy formation. We investigate the impact of different halo merger trees on a semi-analytic model. We find that the z=0 galaxy properties in our model show differences between trees when using a common parameter set. The star formation history of the Universe and the properties of satellite galaxies can show marked differences between trees with different construction methods. Independently calibrating the semi-analytic model for each tree can reduce the discrepancies between the z=0…
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Taxonomy
TopicsInnovation Diffusion and Forecasting
