CTC Seminar: Ana Maria Delgado (JHU), Estimating the triaxiality of massive clusters from 2D observables in MillenniumTNG with machine leaning

Estimating the triaxiality of massive clusters from 2D observables in MillenniumTNG with machine leaning

Ana Maria Delgado 
Post Doctoral Fellow

Abstract: Properties of massive galaxy clusters, such as mass abundance and concentration, are sensitive to cosmology, making cluster statistics a powerful tool for tightening constraints on cosmological parameters. However, the standard approach of favoring a spherically symmetric shape with which to model galaxy clusters can lead to biases in the estimates of cluster properties. In this work, we present a machine-learning approach for estimating the 3D triaxiality of massive galaxy clusters from 2D observables. We utilize the flagship volume of the cosmological-hydrodynamical MillenniumTNG (MTNG) simulation suite as our ground truth. Our model combines the feature extracting power of a convolutional neural network (CNN) and the message passing power of a graph neural network (GNN) in a multi-modal fusion network. Our network achieves triaxial shape and orientation estimates which model MTNG clusters at a ~30% improvement compared to spherically symmetric approximations. Our results provide optimistic outcomes for improved measurements of clusters which populate the high end of the halo-mass function.

Host: Ankita Bera