Machine Learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations

Abstract

In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter only cluster-size halos. The training set is built from THE THREE HUNDRED project which consists of a series of zoomed hydrodynamical simulations of cluster-size regions extracted from the 1 Gpc volume MultiDark dark-matter only simulation (MDPL2). We use as target variables a set of baryonic properties for the intra cluster gas and stars derived from the hydrodynamical simulations and correlate them with the properties of the dark matter halos from the MDPL2 N-body simulation. The different ML models are trained from this database and subsequently used to infer the same baryonic properties for the whole range of cluster-size halos identified in the MDPL2. We also test the robustness of the predictions of the models against mass resolution of the dark matter halos and conclude that their inferred baryonic properties are rather insensitive to their DM properties which are resolved with almost an order of magnitude smaller number of particles. We conclude that the ML models presented in this paper can be used as an accurate and computationally efficient tool for populating cluster-size halos with observational related baryonic properties in large volume N-body simulations making them more valuable for comparison with full sky galaxy cluster surveys at different wavelengths. We make the best ML trained model publicly available.

20 pages, 10 figures, published in MNRAS

References in corpus (15)

• Deep Learning in Neural Networks: An Overview
• The EAGLE project: Simulating the evolution and assembly of galaxies and their environments
• Introducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe
• Simba: Cosmological Simulations with Black Hole Growth and Feedback
• A primer on hierarchical galaxy formation: the semi-analytical approach
• The redshift evolution of massive galaxy clusters in the MACSIS simulations
• \textsc{The Three Hundred} project: The \textsc{Gizmo-Simba} run
• A machine learning approach to mapping baryons onto dark matter haloes using the EAGLE and C-EAGLE simulations
• A Deep Learning Approach to Infer Galaxy Cluster Masses from Planck Compton$-y$ parameter maps
• The scatter in the galaxy-halo connection: a machine learning analysis
• Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks
• The Three Hundred Project: the stellar and gas profiles
• From EMBER to FIRE: predicting high resolution baryon fields from dark matter simulations with Deep Learning
• Multi-Epoch Machine Learning 1: Unravelling Nature vs Nurture for Galaxy Formation
• Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes

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• MultiCAM: A multivariable framework for connecting the mass accretion history of haloes with their properties
• Identifying Galaxy Cluster Mergers with Deep Neural Networks using Idealized Compton-y and X-ray maps
• Multi-Epoch Machine Learning 2: Identifying physical drivers of galaxy properties in simulations
• Caustic and hydrostatic mass bias: Implications for modified gravity
• Prospects for studying the mass and gas in protoclusters with future CMB observations
• The Three Hundred : contrasting clusters galaxy density in hydrodynamical and dark matter simulations
• Generating Galaxy Clusters Mass Density Maps from Mock Multiview Images via Deep Learning
• Deep Learning generated observations of galaxy clusters from dark-matter-only simulations
• Predicting Halo Formation Time Using Machine Learning
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