  This work explores the ability of computer vision algorithms to characterise
dark matter haloes formed in different models of structure formation. We
produce surface mass density maps of the most massive haloes in a suite of
eight numerical simulations, all based on the same initial conditions, but
implementing different models of gravity. This suite includes a standard
$\Lambda$CDM model, two variations of $f(R)$-gravity, two variations of
Symmetron gravity and three Dvali, Gabadadze and Porrati (DGP) models. We use
the publicly available WND-CHARM algorithm to extract 2919 image features from
either the raw pixel intensities of the maps, or from a variety of image
transformations including Fourier, Wavelet, Chebyshev and Edge transformations.
After discarding the most degenerate models, we achieve more than 60%
single-image classification success rate in distinguishing the four different
models of gravity while using a simple weighted neighbour distance (WND) to
define our classification metric. This number can be increased to more than 70%
if additional information, such as a rough estimate of the halo mass, is
included. We find that the classification success steeply declines when the
noise level in the images is increased, but that this trend can be largely
reduced by smoothing the noisy data. We find Zernike moments of the Fourier
transformation of either the raw image or its Wavelet transformation to be the
most descriptive feature, followed by the Gini coefficient of several
transformations and the Haralick and Tamura textures of the raw pixel data
eventually pre-processed by an Edge transformation. The proposed methodology is
general and does not only apply to the characterisation of modified gravity
models, but can be used to classify any set of models which show variations in
the 2D morphology of their respective structure.
