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SubDLe: identification of substructures in cosmological simulations with deep learning by Esposito, Michela; Borgani, Stefano; Murante, Giuseppe is a scholarly article available to read on EtoBox.

What is SubDLe: identification of substructures in cosmological simulations with deep learning about?

The identification of substructures within halos in cosmological hydrodynamical simulations is a fundamental step to identify the simulated counterparts of real objects, namely galaxies. For this reason, substructure finders play a crucial role in extracting relevant information from the simulation outputs. They are based on physically-motivated definitions of substructures, performing multiple steps of particle-by-particle operations, thus computationally expensive. The purpose of this work is to develop a fast algorithm to identify substructures in simulations. The final aim, besides a faster production of subhalo catalogues, is to provide an algorithm fast enough to be applied with a fine time-cadence during the evolution of the simulations. We chose to apply the architecture of a well known Fully Convolutional Network, U-Net, to the identification of substructures within the mass density field of the simulation. We have developed SubDLe (Substructure identification with Deep Learning), an algorithm which combines a 3D generalization of U-Net and a Friends-of-Friends algorithm, and trained it to reproduce the identification of substructures performed by the SubFind algorithm in

Author

Esposito, Michela; Borgani, Stefano; Murante, Giuseppe

Published

2024

Language

EN