Dark Matter Flow: Correlation-Based Statistics from Cosmological N-Body Simulation

A correlation-based statistical dataset derived from cosmological N-body simulations of dark matter flow. It contains pairwise statistical measures for particle separations, including density, velocity, acceleration, energy, and momentum, with redshift and scale dependence. The dataset was created by Zhijie Xu of the Pacific Northwest National Laboratory and is available via paperswithcode, Github, and Zenodo.

Use Cases

• Analyzing dark matter halo mass functions based on inverse mass cascade concepts described in the dataset's theory.
• Studying the redshift evolution of dark matter density and velocity correlations based on pairwise statistical measures.
• Investigating the origin of MOND acceleration based on descriptions of acceleration fluctuation and energy cascade.
• Modeling the baryonic-to-halo mass relation using principles of mass and energy cascade in dark matter flow.

Strengths

• Dataset is part of a two-part series (Halo-based and Correlation-based statistics) allowing for comparative study.
• Derived from cosmological N-body simulations, a standard method for modeling dark matter.
• Accompanied by multiple linked research papers and presentations detailing the underlying cascade theory.

Limitations

• Row count, file size, and specific column definitions are unknown, limiting suitability assessment.
• Last update date is unknown; freshness unverified.
• Column-level documentation is absent; field semantics must be inferred from accompanying theory papers.

Provenance

Source

Pacific Northwest National Laboratory

Collection Method

Calculated from cosmological N-body simulations using a correlation-based statistical approach involving pairwise averages.