Tensor classification of structure in smoothed particle hydrodynamics density fields
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As hydrodynamic simulations increase in scale and resolution, identifying structures with non-trivial geometries or regions of general interest becomes increasingly challenging. There is a growing need for algorithms that identify a variety of different features in a simulation without requiring a ‘by eye’ search. We present tensor classification as such a technique for smoothed particle hydrodynamics (SPH). These methods have already been used to great effect in N-Body cosmological simulations, which require smoothing defined as an input free parameter. We show that tensor classification successfully identifies a wide range of structures in SPH density fields using its native smoothing, removing a free parameter from the analysis and preventing the need for tessellation of the density field, as required by some classification algorithms. As examples, we show that tensor classification using the tidal tensor and the velocity shear tensor successfully identifies filaments, shells and sheet structures in giant molecular cloud simulations, as well as spiral arms in discs. The relationship between structures identified using different tensors illustrates how different forces compete and co-operate to produce the observed density field. We therefore advocate the use of multiple tensors to classify structure in SPH simulations, to shed light on the interplay of multiple physical processes.
Forgan , D , Bonnell , I , Lucas , W & Rice , K 2016 , ' Tensor classification of structure in smoothed particle hydrodynamics density fields ' Monthly Notices of the Royal Astronomical Society , vol 457 , no. 3 , pp. 2501-2513 . DOI: 10.1093/mnras/stw103
Monthly Notices of the Royal Astronomical Society
© 2016 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. This work is made available online in accordance with the publisher’s policies. This is the final published version of the work, which was originally published at: https://dx.doi.org/10.1093/mnras/stw103
DescriptionDF, IB and WL gratefully acknowledge support from the ‘ECOGAL’ ERC advanced grant. KR gratefully acknowledges support from STFC grant ST/M001229/1.
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