Dark Matter Flow Dataset: Halo-Based Statistics from N-Body Simulation

Zhijie Xu from Pacific Northwest National Laboratory created this dataset of dark matter halo properties from cosmological N-body simulations. The dataset presents the redshift and mass scale dependence of halo properties like momentum, energy, size, shape, velocity, and acceleration. It is part of a two-part series on dark matter flow statistics and is available on platforms like Zenodo and GitHub.

Use Cases

• Study halo mass functions and density profiles based on mass and energy cascade concepts mentioned in the description.
• Analyze the baryonic-to-halo mass relation based on mass and energy cascade in dark matter flow.
• Investigate the origin of MOND acceleration based on acceleration fluctuation and energy cascade.
• Model the evolution of supermassive black holes based on energy cascade theory referenced in the description.

Strengths

• Dataset is part of a structured two-part series (Part I: Halo-based, Part II: Correlation-based) for systematic analysis.
• Derived from cosmological N-body simulations, a standard method in the field.
• Properties are presented with explicit dependence on redshift and halo mass scale.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Pacific Northwest National Laboratory

Collection Method

Extracted from cosmological N-body simulations using a halo-based statistical approach.