Estimating the Triaxiality of Massive Clusters from 2D Observables in MillenniumTNG with Machine Learning

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Abstract Properties of massive galaxy clusters, such as mass abundance and concentration, are sensitive to cosmology, making cluster statistics a powerful tool for cosmological studies. However, favoring a more simplified, spherically symmetric model for galaxy clusters can lead to biases in the estimates of cluster properties. In this work, we present a deep learning approach for estimating the triaxiality and orientations of massive galaxy clusters (those with masses ≳10 14 M ⊙ h −1 ) from 2D observables. We utilize the flagship hydrodynamical volume of the suite of cosmological-hydrodynamical MillenniumTNG (MTNG) simulations as our ground truth. Our model combines the feature extracting power of a convolutional neural network and the message passing power of a graph neural network in a multimodal, fusion network. Our model is able to extract 3D geometry information from 2D idealized cluster multiwavelength images (soft X-ray, medium X-ray, hard X-ray, and tSZ effect) and mathematical graph representations of 2D cluster member observables (line-of-sight radial velocities, 2D projected positions and V -band luminosities). Our network improves cluster geometry estimation in MTNG by 30% compared to assuming spherical symmetry. We report an R 2 = 0.85 regression score for estimating the major axis length of triaxial clusters and correctly classifying 71% of prolate clusters with elongated orientations along our line of sight.

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