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9 results for “dark matter halo”
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>
Erratum: Constraints on dark matter-nucleon effective couplings in the presence of kinematically distinct halo substructures using the DEAP-3600 detector [Phys. Rev. D 102, 082001 (2020)]
<p>Corrections to the results from O<sub>3</sub> operator in <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.102.082001">Phys. Rev. D 102, 082001</a>. "Constraints on dark matter-nucleon effective couplings in the presence of kinematically distinct halo substructures using the DEAP-3600 detector".</p>
Crater 2: An Extremely Cold Dark Matter Halo
<p>supplementary data products, including all sky-subtracted spectra from individual targets, as well as random draws from posterior PDFs for model parameters (see enclosed README file)</p> <p> </p>
Correlation between halo properties and dark matter
<p>Pearson correlation coefficients between halo properties (panel a), the local dark matter density with halo virial mass<br> (panel b) and velocity dispersion (panel c), measured in bins of local density for different cosmic-web types, as obtained from the SLICS reference simulation. The error bars denote the standard<br> deviation obtained from the set of catalogs</p>
Slices of dark matter, halo and galaxy density fields
<p>Slices of 25 Mpc h−1 thick though different density fields involved in the calibration of the BAM products and its products. The<br> bottom panel shows the reconstruction of the halo number density field using different models of halo-bias (see §2.6). The rightmost<br> column shows galaxy density field from the reference and from BAM, built by populating halo catalogs with a model of halo occupation<br> distribution</p>
Exploring the halo occupation of AGN using dark-matter cosmological simulations
<p>A semi-empirical model is presented that describes the distribution of Active Galactic Nuclei (AGN) on the cosmic web. It populates dark-matter halos in N-body simulations (MultiDark) with galaxy stellar masses using empirical relations based on abundance matching techniques, and then paints accretion events on these galaxies using state-of-the-art measurements of the AGN occupation of galaxies. The explicit assumption is that the large-scale distribution of AGN is independent of the physics of black-hole fueling. The model is shown to be consistent with current measurements of the two-point correlation function of AGN samples. It is then used to make inferences on the halo occupation of the AGN population. Mock AGN are found in halos with a broad distribution of masses with a mode of $\approx 10^{12}\,h^{-1} \, M_{\odot}$ and a tail extending to cluster-size halos. The clustering properties of the model AGN depend only weakly on accretion luminosity and redshift. The fraction of satellite AGN in the model increases steeply toward more massive halos, in contrast with some recent observational results. This discrepancy, if confirmed, could point to a dependence of the halo occupation of AGN on the physics of black-hole fueling.</p>
MCMC Chains and Maximum Likelihood Parameters for a Random Walk Model of Dark Matter Halo Spins
<p>MCMC chains and the maximum likelihood model parameter file associated with the random walk dark matter halo spin model of Benson, Behrens, & Lu (2020; https://arxiv.org/abs/2001.09208). See the README file for details.</p>
MCMC Chains and Maximum Likelihood Parameters for a Random Walk Model of Dark Matter Halo Concentrations
<p>MCMC chains and the maximum likelihood model parameter file associated with the random walk dark matter halo concentration model of Johnson, Benson, & Grin (2020; https://arxiv.org/abs/2006.15231). See the README file for details.</p>
Dark Matter Halo Merger Trees
<p>This dataset is generated from the Genesis suite of dark matter only N-body simulations (C. Power et al in prep). L10_N2048 is a periodic cubical simulation of side 10 h^-1 Mpc, with 2048^3 particles each of mass 1.0e4 h^-1 solar units. It is used as the initial condition for models presented in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240107396V" target="_blank" rel="noopener">E. M. Ventura et al. (2024)</a> S. Balu, along with input from C. Power ran the simulation using the SWIFT (<a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230513380S" target="_blank" rel="noopener">Schaller et al. 2023</a>) cosmological code, identified haloes using the six-dimensional phase-space halo-finder VELOCIraptor (<a href="https://ui.adsabs.harvard.edu/abs/2019ascl.soft11020E" target="_blank" rel="noopener">Elahi et al. 2019a</a>), and constructed the merger trees with TREEFROG (<a href="https://ui.adsabs.harvard.edu/abs/2019ascl.soft11021E" target="_blank" rel="noopener">Elahi et al. 2019b</a>). During the halo-finding, Friends-of-Friends group require a minimum of 32 particles and the subhaloes need atleast 20 particles. S. Balu converted the original files into the format required by <a href="https://github.com/meraxes-devs/meraxes" target="_blank" rel="noopener">Meraxes</a> using codes developed by S. Mutch with help from Y. Qin and E. M. Ventura.</p> <p>After decompressing, the dataset has the following structure.</p> <pre>├── a_list.txt (the scale factor at all 120 snapshots) ├── grids (average density and velocity) │ ├── snap │ ├── snap_0000.hdf5 │ ├── snap_0001.hdf5 │ ├── ... │ ├── snap_0119.hdf5 └── trees ├── meraxes_augmented_stats.h5 (forest information) └── trees_trimmed.hdf5 (halo properties)</pre>
ScienceDex guides
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