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82 results for “Cosmology”
Higher orders for cosmological phase transitions: a global study in a Yukawa model
<p>Data used in the article with preprint title: <a href="https://arxiv.org/abs/2310.02308">Higher orders for cosmological phase transitions: a global study in a Yukawa model by Oliver Gould and Cheng Xie</a></p><p>Contains file: dataPublishedExport.csv, which consists of the variable used in, and evaluations from the global parameter scan. More details of the content are specified in README.txt.</p>
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>
Dark matter flow dataset Part II: Correlation-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 correlation-based statistical dataset, i) all particle pairs with any given separation r in a N-body system are identified; ii) statistical measures are calculated over all particle pairs with the same separation r (pairwise average); iii) the redshift (z) and scale (r) dependence of all statistical measures (correlation/moment/structure/dispersion/spectrum functions for density, velocity and potential 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>
RESCUER: Cosmological K-corrections for star clusters
<p>**RESCUER: Cosmological K-corrections for star clusters**<br>Authors: Marta Reina-Campos and William E. Harris<br>Date: May 2024</p> <p>Manuscript arXiV ID: arXiv:2310.02307 -- Accepted by MNRAS on May 2024</p> <p>* These tables contain the K-corrections and their uncertainties calculated for star clusters using the E-MILES stellar library.<br>* The authors assumed that star clusters are well represented by single-age and metallicity simple stellar populations (SSPs) described by the BaSTi stellar isochrones and the Chabrier 2003 initial mass function.<br>* Each table corresponds to the K-corrections and their uncertainties for a given combination of filters. The authors considered eleven broad-band filters from the HST/ACS and the JWST/NIRCam cameras. <br>* The uncertainties are estimated using all models within 0.3 dex and 20% in metallicity and age space, respectively, of the target model, and they correspond to the distances to the 10-90th percentiles of the K-corrections of these models.<br>* The tables labeled "csv_homo_filter_X_" correspond to homochromatic K-corrections within the wavelength range of the filter X, whereas those labeled "csv_hetero_filter_X_filter_Y_" contain the K-correction from the observed filter X to the rest-frame filter Y.</p> <p>Within every table:<br>* The K-corrections are given in AB mags<br>* The first column represents the redshift at which the K-correction has been calculated<br>* All of the subsequent columns correspond to the redshift evolution of the K-correction (_target) and their asymmetric uncertainties (_lower and _upper) for a given stellar population, as indicated at the top<br>* The stellar populations are labeled as in the E-MILES stellar library: e.g. "Ech1.30Zm2.27T01.0000", corresponds to a SSP of [M/H] = -2.27 and 1 Gyr old<br>* Dummy values of -100 are placed in the redshifts larger than than the one allowed by the Planck 2018 cosmology.<br>* When an uncertainty equals zero indicates that the target K-correction was smaller/larger than the 10th/90th percentile of the distribution of K-corrections. This typically occurs in models at the edge of the grid of models (i.e. at a corner).</p>
Evolution of cosmic star formation in the SCUBA-2 Cosmology Legacy Survey
<p>This dataset consists of tabulated data from the figures included in the referenced publication. The following datasets are included:</p> <p>Stacked SFR obscuration (IRX=IR/UV) of UVJ-selected star-forming galaxies:</p> <ul> <li>Weighted mean IRX as a function of Muv & stellar mass (Figure 12): MUV_irx1.dat</li> <li>Weighted mean IRX as a function of beta, over all masses and redshifts: beta_irx.dat</li> <li>Weighted mean IRX as a function of beta, binned by stellar mass (Figure 13): beta_irx_mstar.dat</li> <li>Weighted mean IRX as a function of beta, binned by redshift (Figure 14): beta_irx_z.dat</li> </ul> <p>Cosmic SFR density as a function of redshift for massive galaxies log(Ms/Msol)>10 (Figure 15):</p> <ul> <li>All mass-selected galaxies: sfrd_massive.dat</li> <li>UV-luminous galaxies Muv<M*; log(Ms/Msol)>10: sfrd_hiLUV.dat</li> <li>IR-luminous galaxies detected at 450µm: sfrd_IRdet.dat</li> </ul> <p> </p> <p>Cosmic SFR density as a function of redshift corrected to all stellar masses (Figure 16):</p> <ul> <li>All mass-selected galaxies: sfrd_uvlfcorr.dat</li> <li>UV-luminous galaxies Muv<M*; log(Ms/Msol)>10: sfrd_hiLUV_uvlfcorr.dat</li> </ul> <p>Full details of the binning and stacking methodology are explained in the paper.</p>
On the Constraints on Superconducting Cosmic Strings from 21-cm Cosmology (supplementary inference products)
<p>These are the nested sampling inference products that were used to compute the results for <a href="https://arxiv.org/abs/2312.08828">arXiv:2312.08828</a>.</p> <p>The python script, and utility functions, required to produce most of the figures in the paper are included to demonstrate usage. Plotting script for functional posteriors are not included as these require emulators that are not part of this data release. </p> <p>All the included chains were computed using <a href="https://github.com/PolyChord/PolyChordLite">PolyChordLite</a> .</p> <p>Chain foldername conventions:</p> <ul> <li>HERA: Constraints from HERA Phase I 21-cm power spectrum upper limits</li> <li>SARAS_3: Constraints from the SARAS 3 21-cm global signal null detecetion</li> <li>Xray_Background: Constraints from collated measurements of the unresolved X-ray background</li> <li>HERA_SARAS_3_Xray_Background: Joint analysis of the above</li> </ul> <p>Chains have rootnames that are of the form 'foldername_constraints'. See <a href="https://arxiv.org/abs/2312.08828">arXiv:2312.08828</a> for additional details on each of the model parameters. </p> <p>Software used: <a href="https://numpy.org/">numpy</a>, <a href="https://pandas.pydata.org/">pandas</a>, <a href="https://pyyaml.org/">pyYAML</a>, <a href="https://scipy.org/">scipy</a>, <a href="https://github.com/htjb/globalemu">globalemu</a>, <a href="https://matplotlib.org/stable/">matplotlib</a>, <a href="https://www.tensorflow.org/">tensorflow</a>, <a href="https://scikit-learn.org/stable/">scikit-learn</a>, <a href="https://joblib.readthedocs.io/en/stable/">joblib</a>, <a href="https://github.com/PolyChord/PolyChordLite">pypolychord</a>, <a href="https://github.com/handley-lab/anesthetic">anesthetic</a>,<a href="https://github.com/HERA-Team/hera_pspec"> hera-pspec</a>, <a href="https://seaborn.pydata.org/">seaborn</a>, <a href="https://github.com/htjb/margarine">margarine</a>, <a href="https://github.com/handley-lab/fgivenx">fgivenx</a>, <a href="https://github.com/tqdm/tqdm">tqdm</a></p> <p>Exact software versions are specified in an included requirements.txt file for reproducibility. </p>
Supplementary data release for "Cosmology and modified gravitational wave propagation from binary black hole population models"
<p>We release the data products associated to the paper <a href="https://arxiv.org/abs/2112.05728">"Cosmology and modified gravitational wave propagation from binary black hole population models", </a><a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.064030"><em>Phys.Rev.D</em> 105 (2022) 6 </a>.</p> <p>The data can be used in conjunction with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> to reproduce the results of the paper. </p> <p>The data product contains the following folders:</p> <p>* injections_GWTC3: injections used to analyze the GWTC3 catalog, generated with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> . Injections are available separately for O1-O2, O3a, O3b for minimum SNR of 10, 11, 12 (folder names are self-explicative). Each folder contains a file named selected.h5 with the injections. For loading them, refer to the tutorial of the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> .</p> <p>* mock_BPL_5yr_GR : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to General Relativity (see the paper for details)</p> <p>* mock_BPL_5yr_MG : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to a modified gravity model with modified gravitational-wave propagation (see the paper for details)</p> <p>* injections_mock : injections for analyzing the mock datasets above</p>
Supplementary Data: Cosmological constraints on decaying axion-like particles: a global analysis
<p><strong>Supplementary Data</strong></p> <p><em>Cosmological constraints on decaying axion-like particles: a global analysis</em></p> <p>This record contains the supplemetary data for the GAMBIT article, "Cosmological constraints on decaying axion-like particles: a global analysis". </p>
Gravitational waves from a cosmological vacuum phase transition - scalar field value
<p>Movie based on Figure 2 of the paper <a href="https://doi.org/10.1103/PhysRevD.97.123513">Gravitational waves from vacuum first-order phase transitions: from the envelope to the lattice</a> [<a href="https://arxiv.org/abs/1802.05712">arXiv:1802.05712</a>]. This movie originally appeared as Supplemental Material associated with the paper.</p> <p><em>Caption based on original figure caption</em>: Slices through a simultaneous nucleation simulation with parameters <span class="math-tex">\(R_\mathrm{c} M = 7.15\)</span>, <span class="math-tex">\(N_\mathrm{b} = 64\)</span> and <span class="math-tex">\(R_* M = 56.32\)</span> showing the expansion, collision, and oscillatory phase of the scalar field. The scalar field value is shown in blue, and the gravitational wave energy density is shown in red. Note that the range of the colourbar for the gravitational wave energy density changes with time. During the oscillatory phase the gravitational wave energy density becomes very uniform and the “hotspots” are deviations on the sub percent level.</p> <p>The movie is also available on Vimeo, <a href="https://vimeo.com/255031420">here</a>.</p>
Photometric Redshifts for Cosmology: Improving accuracy and uncertainty estimates using Bayesian Neural Networks
<p><strong>This data consists of 286,401 with broad-band g,r,i,z,y photometry from the HSC DR2 survey and spectroscopic redshifts. The majority of galaxies in our sample lies between redshift of 0.01 and 2.5</strong></p>
Cosmological Initial Conditions (gas density and velocity) for 85Mpc^3
<p>Files representing the initial conditions at z=40 for ENZO-MHD cosmological simulation of a comoving 85Mpc^3 volume, simulated with 1024^3 cells and 1024^3 DM particles. The data are in hdf5 format and they were generated using the mpgrafic code.</p> <p>More details of the simulations and on it cosmological parameter can be found at:</p> <ul> <li>https://ui.adsabs.harvard.edu/abs/2021Galax...9..109V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.5350V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2017CQGra..34w4001V/abstract</li> </ul>
Cosmological Initial Conditions (dark matter particles) for 85Mpc^3
<p>Files representing the initial conditions of Dark Matter Particles at z=40 for ENZO-MHD cosmological simulation of a comoving 85Mpc^3 volume, simulated with 1024^3 cells and 1024^3 DM particles. The data are in hdf5 format and they were generated using the mpgrafic code.</p> <p>More details of the simulations and on it cosmological parameter can be found at:</p> <ul> <li>https://ui.adsabs.harvard.edu/abs/2021Galax...9..109V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.5350V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2017CQGra..34w4001V/abstract</li> </ul>
A fresh look at the gravitational-wave signal from cosmological phase transitions
<p>Supplemental material for the paper of the same name, arXiv:1909.11356 [hep-ph], consisting of (1) the set of model benchmark points used in our analysis and (2) our Jupyter notebook for generating the peak-integrated sensitivity plots shown in the paper.</p>
Supplementary Data: Strengthening the bound on the mass of the lightest neutrino with terrestrial and cosmological experiments (arXiv:2009.03287)
<p><strong>Supplementary Data</strong></p> <p><em>Strengthening the bound on the mass of the lightest neutrino with terrestrial and cosmological experiments (arXiv:2009.03287)</em></p> <p>The files in this record contain data from the scans of the models considered in the <a href="http://gambit.hepforge.org">GAMBIT</a> paper on neutrino masses.</p> <p>The files consist of</p> <ul> <li>21 <code>.yaml</code> files corresponding to different models, sampling parameters and/or priors</li> <li>11 final <code>.hdf5</code> files, containing the results of running GAMBIT with each yaml file</li> <li>10 <code>.margestats</code> files containing 1D credible regions for parameters and observables, obtained by running <a href="https://github.com/cmbant/getdist">getdist</a> on the hdf5 files</li> <li>An example file 3-NHB_Neff2_PC500_pp.pip file for plotting the results of a single hdf5 file with <a href="github.com/patscott/pippi">pippi</a></li> <li>A tarball including all files in this record except the hdf5 files.</li> </ul> <p>The different yaml, hdf5 and margestats files corresponding to different models, priors or setttings follow the naming scheme <code>[scan index]-[hierarchy][m_nu0 prior]_[Neff prior]_[scanner]_[extra]_[step].[extension]</code>, where</p> <ul> <li>scan index = <code>1</code>-<code>11</code></li> <li>hierarchy = <code>NH</code> (normal hierarchy), <code>IH</code> (inverted hierarchy)</li> <li>m_nu0 pior = <code>A</code> (linear-log prior on m_nu0), <code>B</code> (linear prior on m_nu0)</li> <li>Neff prior = <code>0</code> (Delta N_eff = 0), <code>1</code> (Delta N_eff > 0), <code>2</code> (Delta N_eff free)</li> <li>scanner = <code>PC500</code> (Polychord with 500 live points), <code>DIV10k</code> (Diver with NP=1e4)</li> <li>extra = blank (standard likelihood combination), <code>Lyalpha</code> (likelihood also includes eBOSS DR14 Lyman-alpha BAO scale measurements)</li> <li>step = blank (main scan), <code>pp</code> (postprocessing of outputs of main scan).</li> <li>extension = <code>yaml</code>, <code>margestats</code>, <code>hdf5/hdf5.tar.gz</code></li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>The YAML files are designed to work with the tagged release of GAMBIT 1.5.0, and the pip file is tested with pippi 2.1. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip file is an example only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please do not expect the same level of polish as for files provided here or in the GAMBIT repo.</p> </li> </ol>
Data release - A Standard Siren Cosmological Measurement from the Potential GW190521 Electromagnetic Counterpart ZTF19abanrhr
<p>Data release accompanying the manuscript</p> <p> "<strong>A Standard Siren Cosmological Measurement from the Potential GW190521 Electromagnetic Counterpart ZTF19abanrhr</strong>" - <a href="https://arxiv.org/abs/2009.14057">Chen et al. (2020)</a></p> <p>assuming an association between the LIGO-Virgo gravitational wave signal <a href="https://www.gw-openscience.org/eventapi/html/O3_Discovery_Papers/GW190521/">GW190521</a> and the electromagnetic signal ZTF19abanrhr as identified by <a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.251102">Graham et al 2020</a>.</p> <p>The posterior samples for the GW analyses are available from <a href="https://doi.org/10.5281/zenodo.4057130">Isi (2020)</a> and <a href="https://dcc.ligo.org/LIGO-P2000158/public">LVC (2020)</a> respectively.</p>
The imprint on the cosmic microwave background (CMB) of Bianchi cosmologies
<p>These animations display the imprint that homogeneous but anisotropic Bianchi models induce in the cosmic microwave background (CMB).</p> <p> </p> <p> </p> <p>scalars_movie.mp4 : Bianchi VIIh/VII0 scalar modes, imprint on the CMB for varying morphology parameters (matter and dark-energy density, rotation scale of shear principal axes)</p> <p>vectors_movie.mp4: Bianchi VIIh/VII0 vector modes, imprint on the CMB for varying morphology parameters (matter and dark-energy density, rotation scale of shear principal axes)</p> <p>tensor_movie.mp4: Bianchi VIIh/VII0 regular tensor modes, imprint on the CMB for varying morphology parameters (matter and dark-energy density, rotation scale of shear principal axes)</p> <p>Additional fixed parameters for the three animations above:<br /> cold-dark-matter physical density: 0.112<br /> baryon physical density: 0.226<br /> Pattern orientation additionally fixed to put spiral in full view</p> <p>_____________________________</p> <p>SVTT_movie.mp4: combinations of Bianchi VIIh/VII0 scalar, vector, regular and irregular tensor modes for varying relative amplitudes of these degrees of freedom and phase angle.</p> <p>Additional fixed parameters:<br /> cold-dark-matter physical density: 0.112<br /> baryon physical density 0.0226<br /> matter density: 0.27<br /> dark energy density: 0.7<br /> rotation scale of shear principal axes: 0.5<br /> Pattern orientation additionally fixed to put spiral in full view</p>
The SCUBA-2 Cosmology Legacy Survey
<p>This dataset consists of 850um maps and a catalogue of the seven extragalactic survey fields of the SCUBA-2 Cosmology Legacy Survey (S2CLS): Akari-NEP, COSMOS, UKIDSS-UDS, Lockman Hole North, EGS, SSA22, GOODS-N. The data are described in Geach et al. (2016). The maps include match-filtered (MF) and non-match-filtered (NMF) flux density (calibrated in mJy/beam), instrumental rms (also in mJy/beam) and signal-to-noise ratio. The dataset also includes a catalogue of sources detected at a significance of >=3.5-sigma across the survey. This is Data Release 1. Contact: j.geach@herts.ac.uk for further details.</p>
Data for analysis in "Towards optimal cosmological parameter recovery from compressed bispectrum statistics"
<p>Measures of the three point function extracted from a suite of simulations using 4 different estimators: namely, the bispectrum, modal estimator, integrated bispectrum, line correlation function. Also supplied are power spectrum measures across the same simulations. <br> <br> The measures are done across 200 fiducial and 60 non-fiducial cosmology simulations, at 3 redshifts. Further details on what was done can be attained by reading the document, Overview.md/Overview.pdf, attached to the bundle. Even more details can be acquired by reading the paper this data was prepared for at https://arxiv.org/abs/1705.04392! </p>
Perturbative effective field theory expansions for cosmological phase transitions, dataset
<p>This dataset is the work of Oliver Gould and Tuomas V.I. Tenkanen. It collects the numerical data from the paper <a href="https://arxiv.org/abs/2309.01672">"Perturbative effective field theory expansions for cosmological phase transitions"</a> (2023). It primarily contains data from perturbative calculations of the thermal evolution of the real-triplet extended Standard Model at two benchmark parameter points.</p><p>In addition, for comparison to the perturbative results, we have included data of the scalar quadratic condensates as a function of temperature from the lattice Monte-Carlo simulations of Lauri Niemi, Michael J. Ramsey-Musolf, Tuomas V.I. Tenkanen and David J. Weir, from the paper <a href="https://doi.org/10.1103/PhysRevLett.126.171802">"Thermodynamics of a Two-Step Electroweak Phase Transition"</a> (2020). We thank the authors for granting permission to reproduce this data here.</p><p>Everything is contained within the archive file <a href="https://zenodo.org/api/records/10353066/draft/files/triplet_two_step_data.tar.gz/content">triplet_two_step_data.tar.gz</a>, a tarball compressed with Gzip. For further details and for the context of this dataset, see the above papers. Details of the conventions used in the dataset can be found in the accompanying README.md file.</p>
Inferring cosmology from gravitational waves using non-parametric detector-frame mass distribution: Data Release
<p>Dataset release accompanying Inferring cosmology from gravitational waves using non-parametric detector-frame mass distribution.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.