What to expect from dynamical modelling of cluster haloes II. Investigating dynamical state indicators with Random Forest

Abstract

We investigate the importances of various dynamical features in predicting the dynamical state (DS) of galaxy clusters, based on the Random Forest (RF) machine learning approach. We use a large sample of galaxy clusters from the Three Hundred Project of hydrodynamical zoomed-in simulations, and construct dynamical features from the raw data as well as from the corresponding mock maps in the optical, X-ray, and Sunyaev-Zel'dovich (SZ) channels. Instead of relying on the impurity based feature importance of the RF algorithm, we directly use the out-of-bag (OOB) scores to evaluate the importances of individual features and different feature combinations. Among all the features studied, we find the virial ratio, $\eta$, to be the most important single feature. The features calculated directly from the simulations and in 3-dimensions carry more information on the DS than those constructed from the mock maps. Compared with the features based on X-ray or SZ maps, features related to the centroid positions are more important. Despite the large number of investigated features, a combination of up to three features of different types can already saturate the score of the prediction. Lastly, we show that the most sensitive feature $\eta$ is strongly correlated with the well-known half-mass bias in dynamical modelling. Without a selection in DS, cluster halos have an asymmetric distribution in $\eta$, corresponding to an overall positive half-mass bias. Our work provides a quantitative reference for selecting the best features to discriminate the DS of galaxy clusters in both simulations and observations.

15 pages, 9 figures, Accepted for publication in MNRAS

References in corpus (48)

• The NumPy array: a structure for efficient numerical computation
• Galaxy Groups in the SDSS DR4: I. The Catalogue and Basic Properties
• The spin and shape of dark matter haloes in the Millennium simulation of a LambdaCDM universe
• On the connection between giant radio halos and cluster mergers
• Images, structural properties and metal abundances of galaxy clusters observed with Chandra ACIS-I at 0.1<z<1.3
• On the efficiency and reliability of cluster mass estimates based on member galaxies
• Implementation of the Random Forest Method for the Imaging Atmospheric Cherenkov Telescope MAGIC
• Searching for Cool Core Clusters at High redshift
• A refined sub-grid model for black hole accretion and AGN feedback in large cosmological simulations
• The Correlation of Star Formation Quenching with Internal Galaxy Properties and Environment
• Unifying X-ray Scaling Relations from Galaxies to Clusters
• LoCuSS: A Comparison of Cluster Mass Measurements from XMM-Newton and Subaru - Testing Deviation from Hydrostatic Equilibrium and Non-Thermal Pressure Support
• The quenching of the star formation activity in cluster galaxies
• The Three Hundred Project: Backsplash galaxies in simulations of clusters
• An Extended Halo-based Group/Cluster finder: application to the DESI legacy imaging surveys DR8
• Galaxy Cluster Mass Reconstruction Project: I. Methods and first results on galaxy-based techniques
• Galaxy and Mass Assembly (GAMA): The halo mass of galaxy groups from maximum-likelihood weak lensing
• The distribution function of dark matter in massive haloes
• The Three Hundred Project: Dynamical state of galaxy clusters and morphology from multi-wavelength synthetic maps
• The evolution of the quenching of star formation in cluster galaxies since z $\sim$ 1
• ELUCID IV: Galaxy Quenching and its Relation to Halo Mass, Environment, and Assembly Bias
• Mapping and characterisation of cosmic filaments in galaxy cluster outskirts: strategies and forecasts for observations from simulations
• The ThreeHundred: the structure and properties of cosmic filaments in the outskirts of galaxy clusters
• On the Correlation between Spin Parameter and Halo Mass
• The Three Hundred Project: Ram pressure and gas content of haloes and subhaloes in the phase-space plane
• A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
• A joint SZ-Xray-optical analysis of the dynamical state of 288 massive galaxy clusters
• Galaxy cluster mass estimation with deep learning and hydrodynamical simulations
• The Three Hundred Project: quest of clusters of galaxies morphology and dynamical state through Zernike Polynomials
• The Three Hundred project: shapes and radial alignment of satellite, infalling, and backsplash galaxies
• Detection of missing baryons in galaxy groups with kinetic Sunyaev-Zel'dovich effect
• Cosmic filaments in galaxy cluster outskirts: quantifying finding filaments in redshift space
• Shocks in the Stacked Sunyaev-Zel'dovich Profiles of Clusters I: Analysis with the Three Hundred Simulations
• Diffuse stellar emission in X-ray luminous galaxy clusters at z~0.3 I. Is the diffuse optical light boosted and rejuvenated in merging clusters?
• Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning
• Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks
• The Three Hundred Project: the stellar and gas profiles
• The search for galaxy cluster members with deep learning of panchromatic HST imaging and extensive spectroscopy
• Multiwavelength investigations of co-evolution of bright custer galaxies
• Cross-correlation of Planck CMB lensing with DESI galaxy groups
• What to expect from dynamical modelling of cluster haloes I. The information content of different dynamical tracers
• Deep learning for Sunyaev-Zel'dovich detection in Planck
• DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning
• Emulating Sunyaev-Zeldovich Images of Galaxy Clusters using Auto-Encoders
• Non-Parametric Spherical Jeans Mass Estimation with B-splines
• Measuring Cosmological Distances Using Cluster Edges as a Standard Ruler
• Velocity dispersion vs cluster mass: a new scaling law with The Three Hundred clusters
• Predicting halo occupation and galaxy assembly bias with machine learning

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