The multi-dimensional halo assembly bias can be preserved when enhancing halo properties with HALOSCOPE

Abstract

Over $90$\% of dark matter haloes in cosmological simulations have unresolved properties. This can hinder the dynamical range of simulations and result in systematic biases when modelling cosmological tracers. We aim to more precisely determine unresolved structural and dynamical halo properties while preserving the correlations with environment and halo assembly bias found in simulations. We have developed HALOSCOPE, a machine learning technique that uses multi-variate conditional probability distribution functions. This method ensures that correlations among various halo properties, as well as their dependence on the local environment, are preserved. In this work, we trained HALOSCOPE with a high-resolution (HR) simulation and used it to better determine the properties (concentration, spin, and two shape parameters) of unresolved dark matter haloes in an eight times lower resolution simulation. HALOSCOPE is able to recover the multi-dimensional halo assembly bias, that is, the correlations of different combinations of halo properties with the large-scale environment, measured in the HR simulation. This is achieved by including the linear halo-by-halo bias and tidal anisotropy in the set of input training parameters. HALOSCOPE, by design, also recovers the joint distribution of the halo properties. To study how resolution effects propagate into the clustering of model galaxies, we generated catalogues of central galaxies using two implementations of the assembly bias in a halo occupation distribution model. The clustering of central model galaxies is improved by a factor of three at $0.009<k({\mathrm{Mpc}}^{-1}h)<0.6$ when the unresolved haloes are enhanced with HALOSCOPE. HALOSCOPE can improve the accuracy of cosmological tracer catalogues produced with approximate methods when many realisations are needed.

14 pages, 6 main figures, accepted by A&A, code available at https://github.com/computationalAstroUAM/haloscope

References in corpus (22)

• Euclid. I. Overview of the Euclid mission
• Properties of Dark Matter Haloes in Clusters, Filaments, Sheets and Voids
• The spin and shape of dark matter haloes in the Millennium simulation of a LambdaCDM universe
• A universal model for halo concentrations
• Halo assembly bias and its effects on galaxy clustering
• AbacusSummit: A Massive Set of High-Accuracy, High-Resolution $N$-Body Simulations
• Spatial Clustering of Dark Matter Halos: Secondary Bias, Neighbor Bias, and the Influence of Massive Neighbors on Halo Properties
• Environmental dependence in the ellipsoidal collapse model
• On the Correlation between Spin Parameter and Halo Mass
• Predicting Galaxy Star Formation Rates via the Co-evolution of Galaxies and Halos
• Euclid. V. The Flagship galaxy mock catalogue: a comprehensive simulation for the Euclid mission
• The MillenniumTNG Project: An improved two-halo model for the galaxy-halo connection of red and blue galaxies
• Effects of Inner Halo Angular Momentum on the Peanut/X-shapes of Bars
• What sets the splashback radius of dark matter haloes: accretion history or other properties?
• Multi-wavelength mock galaxy catalogs of the low-redshift Universe
• Mock halo catalogs: assigning unresolved halo properties using correlations with local halo environment
• Impact of tidal environment on galaxy clustering in GAMA
• Galaxy assembly bias in the stellar-to-halo mass relation for red central galaxies from SDSS
• MultiCAM: A multivariable framework for connecting the mass accretion history of haloes with their properties
• A Machine Learning Approach to Correct for Mass Resolution Effects in Simulated Halo Clustering Statistics
• Secondary halo bias through cosmic time II: Reconstructing halo properties using clustering information
• Making use of sub-resolution halos in N-body simulations

Cited by in corpus (1)

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