Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this halo-based statistical dataset, i) all halos in N-body system are identified with all particles divided into halo and out-of-halo particles; ii) halos are grouped into halo groups including all halos of the same mass (m<sub>h</sub>); iii) the redshift (z) and mass scale (m<sub>h</sub>) dependence of all halo properties (momentum, energy, size, shape, velocity, acceleration, etc.) are presented . </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
ShareScore
48/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 8
- Access
- 20
- Reuse readiness
- 8
- Engagement
- 4