ACACIA: a new method to produce on-the-fly merger trees in the RAMSES code. (arXiv:1812.06708v3 [astro-ph.GA] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Ivkovic_M/0/1/0/all/0/1">Mladen Ivkovic</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Teyssier_R/0/1/0/all/0/1">Romain Teyssier</a>

The implementation of ACACIA, a new algorithm to generate dark matter halo
merger trees with the Adaptive Mesh Refinement (AMR) code RAMSES, is presented.
The algorithm is fully parallel and based on the Message Passing Interface
(MPI). As opposed to most available merger tree tools, it works on the fly
during the course of the N body simulation. It can track dark matter
substructures individually using the index of the most bound particle in the
clump. Once a halo (or a sub-halo) merges into another one, the algorithm still
tracks it through the last identified most bound particle in the clump,
allowing to check at later snapshots whether the merging event was definitive,
or whether it was only temporary, with the clump only traversing another one.
The same technique can be used to track orphan galaxies that are not assigned
to a parent clump anymore because the clump dissolved due to numerical
over-merging. We study in detail the impact of various parameters on the
resulting halo catalogues and corresponding merger histories. We then compare
the performance of our method using standard validation diagnostics,
demonstrating that we reach a quality similar to the best available and
commonly used merger tree tools. As a proof of concept, we use our merger tree
algorithm together with a parametrised stellar-mass-to-halo-mass relation and
generate a mock galaxy catalogue that shows good agreement with observational
data.

The implementation of ACACIA, a new algorithm to generate dark matter halo
merger trees with the Adaptive Mesh Refinement (AMR) code RAMSES, is presented.
The algorithm is fully parallel and based on the Message Passing Interface
(MPI). As opposed to most available merger tree tools, it works on the fly
during the course of the N body simulation. It can track dark matter
substructures individually using the index of the most bound particle in the
clump. Once a halo (or a sub-halo) merges into another one, the algorithm still
tracks it through the last identified most bound particle in the clump,
allowing to check at later snapshots whether the merging event was definitive,
or whether it was only temporary, with the clump only traversing another one.
The same technique can be used to track orphan galaxies that are not assigned
to a parent clump anymore because the clump dissolved due to numerical
over-merging. We study in detail the impact of various parameters on the
resulting halo catalogues and corresponding merger histories. We then compare
the performance of our method using standard validation diagnostics,
demonstrating that we reach a quality similar to the best available and
commonly used merger tree tools. As a proof of concept, we use our merger tree
algorithm together with a parametrised stellar-mass-to-halo-mass relation and
generate a mock galaxy catalogue that shows good agreement with observational
data.

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