Tensor classification of structure in smoothed particle hydrodynamics
  density fields
  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 tesselation 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.
