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This work explores the ability of computer vision algorithms to characterisedark matter haloes formed in different models of structure formation. Weproduce surface mass density maps of the most massive haloes in a suite ofeight numerical simulations, all based on the same initial conditions, butimplementing different models of gravity. This suite includes a standard$\Lambda$CDM model, two variations of $f(R)$-gravity, two variations ofSymmetron gravity and three Dvali, Gabadadze and Porrati (DGP) models. We usethe publicly available WND-CHARM algorithm to extract 2919 image features fromeither the raw pixel intensities of the maps, or from a variety of imagetransformations 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 differentmodels of gravity while using a simple weighted neighbour distance (WND) todefine our classification metric. This number can be increased to more than 70%if additional information, such as a rough estimate of the halo mass, isincluded. We find that the classification success steeply declines when thenoise level in the images is increased, but that this trend can be largelyreduced by smoothing the noisy data. We find Zernike moments of the Fouriertransformation of either the raw image or its Wavelet transformation to be themost descriptive feature, followed by the Gini coefficient of severaltransformations and the Haralick and Tamura textures of the raw pixel dataeventually pre-processed by an Edge transformation. The proposed methodology isgeneral and does not only apply to the characterisation of modified gravitymodels, but can be used to classify any set of models which show variations inthe 2D morphology of their respective structure.

Characterising dark matter haloes with computer vision