The four cosmic tidal web elements from the $\beta$-skeleton

Abstract

Precise cosmic web classification of observed galaxies in massive spectroscopic surveys can be either highly uncertain or computationally expensive. As an alternative, we explore a fast Machine Learning-based approach to infer the underlying dark matter tidal cosmic web environment of a galaxy distribution from its $\beta$-skeleton graph. We develop and test our methodology using the cosmological magnetohydrodynamic simulation Illustris-TNG at $z=0$. We explore three different tree-based machine-learning algorithms to find that a random forest classifier can best use graph-based features to classify a galaxy as belonging to a peak, filament or sheet as defined by the T-Web classification algorithm. The best match between the galaxies and the dark matter T-Web corresponds to a density field smoothed over scales of $2$ Mpc, a threshold over the eigenvalues of the dimensionless tidal tensor of ${\lambda }_{\mathrm{th}}=0.0$ and galaxy number densities around $8\times 10^{-3}$ Mpc${}^{-3}$. This methodology results on a weighted F1 score of 0.728 and a global accuracy of 74\%. More extensive tests that take into account lightcone effects and redshift space distortions (RSD) are left for future work. We make one of our highest ranking random forest models available on a public repository for future reference and reuse.

arXiv commentApJ accepted, 13 pages, 9 figures

References in corpus (50)

• Scikit-learn: Machine Learning in Python
• XGBoost: A Scalable Tree Boosting System
• The NumPy array: a structure for efficient numerical computation
• Planck 2015 results. XIII. Cosmological parameters
• Simulating Galaxy Formation with the IllustrisTNG Model
• First results from the IllustrisTNG simulations: matter and galaxy clustering
• First results from the IllustrisTNG simulations: the stellar mass content of groups and clusters of galaxies
• First results from the IllustrisTNG simulations: the galaxy color bimodality
• Properties of galaxies reproduced by a hydrodynamic simulation
• First results from the IllustrisTNG simulations: A tale of two elements -- chemical evolution of magnesium and europium
• Simulating galaxy formation with black hole driven thermal and kinetic feedback
• How Filaments are Woven into the Cosmic Web
• First results from the IllustrisTNG simulations: radio haloes and magnetic fields
• The persistent cosmic web and its filamentary structure I: Theory and implementation
• Properties of Dark Matter Haloes in Clusters, Filaments, Sheets and Voids
• Evolution of the cosmic web
• ZOBOV: a parameter-free void-finding algorithm
• Tracing the cosmic web
• Bayesian physical reconstruction of initial conditions from large scale structure surveys
• A Dynamical Classification of the Cosmic Web
• A Cosmic Watershed: the WVF Void Detection Technique
• NEXUS: Tracing the Cosmic Web Connection
• The spin and orientation of dark matter halos within cosmic filaments
• The Spine of the Cosmic Web
• Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era
• Skeleton as a probe of the cosmic web: the 2D case
• Spatial and Dynamical Properties of Voids in a LCDM Universe
• Disentangling the Cosmic Web I: Morphology of Isodensity Contours
• Bayesian non-linear large scale structure inference of the Sloan Digital Sky Survey data release 7
• Galaxy And Mass Assembly (GAMA): Stellar mass growth of spiral galaxies in the cosmic web
• Reconstructing the cosmic density field with the distribution of dark matter halos
• Galaxy And Mass Assembly (GAMA): The galaxy luminosity function within the cosmic web
• Cosmic Web Reconstruction through Density Ridges: Method and Algorithm
• Galaxy and Mass Assembly (GAMA): halo formation times and halo assembly bias on the cosmic web
• Finding cosmic voids and filament loops using topological data analysis
• T-ReX: a graph-based filament detection method
• Cosmic web dependence of galaxy clustering and quenching in SDSS
• Revealing the Dark Threads of the Cosmic Web
• TARDIS Paper I: A Constrained Reconstruction Approach to Modeling the z~2.5 Cosmic Web Probed by Lyman-alpha Forest Tomography
• Cosmic voids detection without density measurements
• Large Scale Structure in CHILES
• Halo based reconstruction of the cosmic mass density field
• Beta-Skeleton Analysis of the Cosmic Web
• Cosmic web-type classification using decision theory
• Large-scale structures in the $\Lambda$CDM Universe: network analysis and machine learning
• MANTRA: A Machine Learning reference lightcurve dataset for astronomical transient event recognition
• Probabilistic cosmic web classification using fast-generated training data
• A Machine Learning Approach to Galaxy-LSS Classification I: Imprints on Halo Merger Trees
• Bayesian Cosmic Web Reconstruction: BARCODE for Clusters
• Tensor anisotropy as a tracer of cosmic voids

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• A theoretical view of the T-web statistical description of the cosmic web
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• Improving SDSS cosmological constraints through $\beta$-skeleton weighted correlation functions
• Cosmological constraints from the density gradient weighted correlation function
• Enhancing Cosmological Constraints by Two-dimensional $\beta$-cosmic-web Weighted Angular Correlation Functions