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  DASH: A library of dynamical subhalo evolution

Ogiya, G., van den Bosch, F. C., Hahn, O., Green, S. B., Miller, T. B., & Burkert, A. (2019). DASH: A library of dynamical subhalo evolution. Monthly Notices of the Royal Astronomical Society, 485(1), 189-202. doi:10.1093/mnras/stz375.

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Ogiya, Go, Author
van den Bosch, Frank C., Author
Hahn, Oliver, Author
Green, Sheridan B., Author
Miller, Tim B., Author
Burkert, Andreas1, Author           
Affiliations:
1Optical and Interpretative Astronomy, MPI for Extraterrestrial Physics, Max Planck Society, ou_159895              

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 Abstract: The abundance and demographics of dark matter substructure are important for many areas in astrophysics and cosmological N-body simulations have been the primary tool used to investigate them. However, it has recently become clear that the simulations are subject to numerical artefacts, which hampers a proper treatment of the tidal evolution of subhaloes. Unfortunately, no analytical models that accurately describe subhalo evolution exist either. We therefore present a library of idealized, high-resolution N-body simulations of the tidal evolution of individual subhaloes that can be used to calibrate semi-analytical models and to complement cosmological simulations. The simulations focus on minor mergers, i.e. the mass of the subhalo is much smaller than that of the host halo, such that the impact of dynamical friction is negligible. This set-up allows the adoption of a fixed analytical potential for modelling the host halo. The dynamical evolution of subhaloes is followed with N-body computations. In the library, four parameters, two of which characterize the subhalo orbit with respect to the host halo, and the two concentrations of the host- and subhalo, are varied over the ranges encountered in cosmological simulations. We show several representative examples from the library that illustrate the evolution of the subhalo mass and velocity dispersion profiles. Additionally, we make a pre-trained non-parametric model of the subhalo mass evolution based on random forest regression publicly available. This model is able to interpolate the simulation data at the 0.1 dex level and provides efficient access to the data for further use in modelling.1

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 Dates: 2019-02-07
 Publication Status: Published online
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 Rev. Type: Peer
 Identifiers: DOI: 10.1093/mnras/stz375
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Title: Monthly Notices of the Royal Astronomical Society
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Source Genre: Journal
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Publ. Info: OXFORD : OXFORD UNIV PRESS
Pages: - Volume / Issue: 485 (1) Sequence Number: - Start / End Page: 189 - 202 Identifier: ISSN: 0035-8711
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000021470