A Conditional Abundance Matching Method of Extending Simulated Halo Merger Trees to Resolve Low-Mass Progenitors and Sub-halos

Abstract

We present an algorithm to extend subhalo merger trees in a low-resolution dark-matter-only simulation by conditionally matching them to those in a high-resolution simulation. The algorithm is general and can be applied to simulation data with different resolutions using different target variables. We instantiate the algorithm by a case in which trees from ELUCID, a constrained simulation of $(500h^{-1}\mathrm{Mpc}{)}^3$ volume of the local universe, are extended by matching trees from TNGDark, a simulation with much higher resolution. Our tests show that the extended trees are statistically equivalent to the high-resolution trees in the joint distribution of subhalo quantities and in important summary statistics relevant to modeling galaxy formation and evolution in halos. The extended trees preserve certain information of individual systems in the target simulation, including properties of resolved satellite subhalos, and shapes and orientations of their host halos. With the extension, subhalo merger trees in a cosmological scale simulation are extrapolated to a mass resolution comparable to that in a higher-resolution simulation carried out in a smaller volume, which can be used as the input for (sub)halo-based models of galaxy formation. The source code of the algorithm, and halo merger trees extended to a mass resolution of $\sim 2\times 10^8{h}^{-1}{M}_{\odot }$ in the entire ELUCID simulation, are available.

arXiv comment24 pages, 14 figures, 3 tables. MNRAS accepted

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