Find research datasets worth reusing

opencc-by-4.0Dec 2019View details →

Supplementary Material for A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations

Description of the Supplementary Data This record contains tabulated Bayesian and frequentist exclusion limits, profile likelihood maps and posterior probability maps for the publication S. Hoof, A. Geringer-Sameth, and R. Trotta, “A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations,” <a href="https://doi.org/10.1088/1475-7516/2020/02/012">JCAP 02 (2020) 012</a> (also available on the <a href="https://arxiv.org/abs/1812.06986">arXiv</a>). The dwarf spheroidal galaxies considered in this work are (in alphabetical order): Aquarius II, Boötes I, Canes Venatici I, Canes Venatici II, Carina, Carina II, Coma Berenices, Draco, Draco II, Fornax, Grus I, Hercules, Horologium I, Leo I, Leo II, Leo IV, Leo V, Pegasus III, Pisces II, Reticulum II, Sculptor, Segue 1, Sextans, Tucana II, Ursa Major I, Ursa Major II, and Ursa Minor. This record consists of the following files, which correspond to the limits presented Figures 9 and 10 of the paper. The files can be downloaded individually or obtained by downloading and unpacking the <code>record_2612268.zip</code>. In what follows,<code>[CHANNEL]</code> refers to the annihilation channel used, i.e. e+ e-, μ+ μ-, τ+ τ-, b b̄, c c̄, t t̄, g g, W+ W-, and Z Z. We also provide a simple plotting script for <code>Python</code>, named <code>plotting_script.py</code>, which provides basic plotting routines for all files. <ul> <li>One-dimensional limits on <σ v>. The files <code>oneD_frequentist_limits_[CHANNEL]_channel.txt</code> contain the frequentist limits (at 95% confidence level, 1 degree of freedom) given the value of the WIMP mass mχ tabulated there. The files <code>oneD_Bayesian_limits_[CHANNEL]_channel.txt</code> contain the Bayesian limit (95% credibility conditioned on the mass mχ tabulated there).</li> <li>Two-dimensional grid of profile likelihood values. The files <code>twoD_profile_likelihood_map_[CHANNEL]_channel.txt</code> contain the natural logarithm of the profile likelihood w.r.t. the global best-fit likelihood value for that channel together with the corresponding values of mχ and <σ v>. Note that for obtaining the limits in Fig. 10, which are conditioned on the WIMP mass, one needs to rescale the profile likelihood values with the maximum profile likelihood for a given WIMP mass.</li> <li>Two-dimensional grid of posterior probabilities for each combination of mχ and <σ v>. The files <code>twoD_posterior_probability_map_[CHANNEL]_channel.txt</code> contain probabilities (obtained using a log-uniform prior on <σ v>) together with the corresponding values of mχ and <σ v>. The tabulated values of mχ and <σ v> correspond to the centres of the respective bins in mχ and <σ v> and the posterior probability contained in them (the total posterior probability sums to 1).</li> </ul> Please contact the authors if you require different data or have any questions regarding this data set.

opencc-by-4.0Jun 2024View details →

TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising

TIDMAD is the first dataset and benchmark from a dark matter physics experiment, providing ultra-long time series data and comprehensive tools that enable machine learning models to directly advance the fundamental physics search for dark matter. This data is availble for download via <code>download_data.py</code>. Metadata for this dataset is specified in <code>TIDMAD_croissant.json</code>. The file names are listed in <code>filelist.dat</code>. For furhter information and publically available code, please see the associated <a href="https://github.com/jessicafry/TIDMAD" target="_blank" rel="noopener">GitHub repository</a>. For more information on this dataset and benchmark, please reference our TIDMAD paper.

opencc-by-4.0Jun 2022View details →

Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation

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 (mh); iii) the redshift (z) and mass scale (mh) dependence of all halo properties (momentum, energy, size, shape, velocity, acceleration, etc.) are presented .  Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution: <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> 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>" The two relevant datasets and accompanying presentation can be found at:  <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> 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>. Cascade and statistical theory developed by these datasets: <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>

opencc-by-4.0Jun 2022View details →

Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation

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.   Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution: <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> 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>" The two relevant datasets and accompanying presentation can be found at:  <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> 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>. Cascade and statistical theory developed by these datasets: <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>

opencc-by-4.0Oct 2020View details →

The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering - Supporting Data

Supporting code and data for the paper:  The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering Raen (2020) Supporting code includes `run_star_extras.f`, inlist templates, and our dark matter module (to be used in conjunction with MESA: <a href="http://mesa.sourceforge.net/index.html">Modules for Experiments in Stellar Astrophysics</a>). The full source code used in the production of this paper is available at <a href="https://github.com/troyraen/DM-in-Stars/">github.com/troyraen/DM-in-Stars</a> in the Raen2020 branch. (The master branch is intended for use by those wishing to use our module to explore DM effects beyond the scope of this paper.) We used MESA version 12115, and MESA SDK version 20190830. Model data includes MESA history and profile data for the models highlighted in the paper (\(1.0\ \mathrm{M}_\odot\) and \(3.5\ \mathrm{M}_\odot\) models with \(\Gamma_B = 0\) (no dark matter), \(\Gamma_B = 10^4\), and \(\Gamma_B = 10^6\)).  The specific inlists used to generate the models are also included. Additional data will be shared on reasonable request to the paper's corresponding author.

opencc-by-4.0May 2017View details →

Supplementary Data: Status of the scalar singlet dark matter model (arXiv:1705.07931)

Supplementary Data Status of the scalar singlet dark matter model arXiv:1705.07931 The files in this record contain data for the scalar singlet dark matter model considered in the GAMBIT "Round 1" scalar singlet paper. The files consist of <ul> <li>Three YAML files, each corresponding to a different parameter range</li> <li>StandardModel_SLHA2_SingletDM_scan_15.yaml, a universal YAML fragment included from the other three YAML files</li> <li>Three hdf5 files. SingletDM.hdf5 contains the combined results of all sampling runs, and is the basis for the profile likelihood plots in the paper. SingletDM_TW_full.hdf5 and SingletDM_TW_lowmass.hdf5 contain the results from T-Walk scans over the full and low-mass parameter ranges, respectively. These are the bases for the marginalised posterior plots in the paper.</li> <li>An example pip file corresponding to each hdf5 file, for producing plots using pippi</li> <li>A tarball best_fits_yaml.tar.gz containing YAML files of the best-fit point in each subregion of the fit.</li> </ul> The YAML files corresponding to different parameter ranges follow the naming scheme SingletDM_[slice].yaml, where slice may be full, lowmass or neck. Each of these YAML files contains entries in the Scanners node for running Diver, MultiNest, TWalk and GreAT. A few caveats to keep in mind: <ol> <li> The YAML files that we give here are updated compared to the ones that we used when generating the hdf5 file, in order to match the set of available options in the release version of GAMBIT 1.0.0. The included physics and numerics are however identical. </li> <li> The YAML files are designed to work with the tagged release of GAMBIT 1.0.0, and the pip file is tested with pippi 2.0, commit 2ab061a8. 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). </li> <li> 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 don't expect the same level of polish as for files provided here or in the GAMBIT repo. </li> </ol>

openmit-licenseDec 2023View details →

Supplementary Data for Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay

This dataset contains the interpolation tables for use with the DM21cm code release as part of "Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay." For details on usage, see the public github repository at: https://github.com/yitiansun/DM21cm.

opencc-by-4.0May 2024View details →

Supplementary data for "Resonant or asymmetric: The status of sub-GeV dark matter"

The files in this record contain supplementary data for the study, "Resonant or asymmetric: The status of sub-GeV dark matter". Samples have been created using <a href="https://gambitbsm.org/" target="_blank" rel="noopener">GAMBIT</a> and figures can be reproduced with <a href="https://github.com/tegonzalo/pippi" target="_blank" rel="noopener">pippi</a>.

opencc-by-4.0Jun 2018View details →

Supplementary Data: Impact of vacuum stability, perturbativity and XENON1T on global fits of Z2 and Z3 scalar singlet dark matter (arXiv:1806.11281)

Supplementary Data   Impact of vacuum stability, perturbativity and XENON1T on global fits of Z2 and Z3 scalar singlet dark matter <a href="https://arxiv.org/abs/1806.xxxxx">arXiv:</a><a href="https://arxiv.org/abs/1806.11281">1806.11281</a> The files in this record contain data for the scalar singlet dark matter models considered in the <a href="http://gambit.hepforge.org">GAMBIT</a> "Scalar singlet Mark II" paper. The files consist of <ul> <li>30 regular YAML files</li> <li><code>StandardModel_SLHA2_scan.yaml</code>, a universal YAML fragment included from the other YAML files</li> <li>14 hdf5 files. 8 of these correspond to the complete set of combined samples for each fit. These 8 fits are generated from all binary permutations of three run properties: Z2 or Z3 model, with or without absolute vacuum stability demanded, and with constraints from the 2017 or 2018 XENON1T data. These 8 hdf5 files are used to generate the profile likelihood plots in the paper. The other 6 hdf5 files are the results of T-Walk runs, and are used to generate the posterior pdfs in the paper.</li> <li>Some example pip files for producing plots from the hdf5 files using <a href="github.com/patscott/pippi">pippi</a></li> <li>A tarball <code>best_fits_yaml.tar.gz</code> containing YAML files of the best-fit point in each of the 8 fits.</li> </ul> The files follow the naming scheme <code>SingletDM_[model]_[slice]_[vacuum]_[xenon]_[prior]_[scanner].yaml</code>. <ul> <li>model: <code>Z2</code> or <code>Z3</code></li> <li>slice: <code>full</code>, <code>lowmass</code>, <code>neck</code> or absent (for hdf5 files)</li> <li>vacuum: <code>ms</code> (metastable) or <code>vs</code> (absolute vacuum stability)</li> <li>prior: <code>logmu3</code>, <code>flatmu3</code> or absent (for Z2 scans)</li> <li>scanner: <code>TWalk</code> or absent (implies Diver scans in the case of YAML files, and indicates merged samples potentially from both Diver and T-Walk in the case of hdf5 files)</li> </ul> A few caveats to keep in mind: <ol> <li> The YAML files are designed to work with GAMBIT 1.2.0, commit e4d3f739, and the pip files are tested with pippi 2.1, commit c094b8c8. 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). </li> <li> The pip files are examples 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 don't expect the same level of polish as for files provided here or in the GAMBIT repo.  </li> </ol>

opencc-by-4.0Oct 2019View details →

New limit on dark photon dark matter from searches using LIGO O1 data.

The three files contain the three limits in Fig.4 of the published paper. For each file, there are 2 columns of data, with the first column being the frequency in Hz and the second column the corresponding upper limit on the dimensionless dark photon squared coupling. limit_red.txt:      the red line, O1 95% CL limits (1800s SFTs)  limit_blue.txt:    the blue line, average optimal O1 limits (893 hours) limit_yellow.txt: the yellow line, Nominal SNR O1 95% CL limits

opencc-by-4.0Oct 2024View details →

Data from: "DarkSide-20k sensitivity to light dark matter particles"

These files provide expected limits from the preprint of arXiv:2407.05813, "DarkSide-20k sensitivity to light dark matter particles" and are made available by the DarkSide-20k Collaboration.

opencc-by-4.0Mar 2023View details →

Supplementary Data: Global fits if simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator

This record contains the YAML files, data files, and some of the plotting scripts for: "Global fits of simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator". Samples have been created using GAMBIT and figures can be reproduced with pippi. Plotting scripts (*.pip) are designed to work with either the original version of pippi 2.1 or the forked unreleased version. The provided scripts do not reproduce all the figures in the paper exactly. To save storage space, all samples have been compressed using <code>tar</code>. To inflate each dataset after downloading run <code>tar -zxvf <samples>.hdf5.gz</code>. To facilitate uploading to Zenodo, several of the data files have been thinned to only include enough points to reproduce plots.

opencc-by-4.0Mar 2023View details →

Cosmological Initial Conditions (dark matter particles) for 85Mpc^3

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. More details of the simulations and on it cosmological parameter can be found at: <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>

opencc-by-4.0May 2023View details →

Supplementary Data: Fast and accurate AMS-02 antiproton likelihoods for global dark matter fits

The files in this record contain supplementary data for the study, "Fast and accurate AMS-02 antiproton likelihoods for global dark matter fits".  Samples have been created using <a href="https://gambitbsm.org/" target="_blank" rel="noopener">GAMBIT</a> and figures can be reproduced with <a href="http://github.com/patscott/pippi" target="_blank" rel="noopener">pippi</a>.

opencc-by-4.0Apr 2020View details →

Earth-scattering likelihoods: Likelihood and p-value tables for reconstructing the local Dark Matter Density

Tables of likelihoods, p-values and best-fits associated with the EarthScatterLikelihood code - <a href="https://github.com/bradkav/EarthScatterLikelihood">https://github.com/bradkav/EarthScatterLikelihood</a> - released alongside the paper "Measuring the local Dark Matter density in the laboratory" (<a href="https://arxiv.org/abs/2004.01621">arXiv:2004.01621</a>). Examples for how to load the files are given in 'EarthScatterLikelihood/plotting'. Simply extract the folders into 'EarthScatterLikelihood/results' in https://github.com/bradkav/EarthScatterLikelihood.

opencc-by-4.0Sep 2020View details →

Likelihoods for the CTA sensitivity to a dark matter signal from the Galactic centre (A. Acharyya et al., [arXiv:2007.16129])

We present likelihoods to estimate upper limits on DM pair-annihilation in the Galactic centre, based on  the Cherenkov Telescope Array (CTA) consortium publication "Sensitivity of the Cherenkov Telescope Array to a dark matter signal from the Galactic centre" [arXiv:2007.16129]. As explained in more detail in Sec. 5.2 of that article, these likelihoods are suitable for models featuring cuspy dark matter profiles and generic gamma-ray spectra produced from annihilating dark matter. The four files contain likelihoods that have been derived with respect to the full CTA South baseline array layout and the initial construction phase of CTA South. For each of these two cases, as indicated by the filename, we provide tables with and without the inclusion of systematic uncertainties (where the former refers to the benchmark treatment of systematic uncertainties as described in the main publication). Further, all likelihoods are based on the benchmark analysis settings with respect to masking known bright gamma-ray sources and the adopted interstellar emission models.

opencc-by-4.0Jun 2020View details →

Stealth dark matter confinement transition and gravitational waves --- data release

This HDF5 file collects data and analysis results for non-perturbative lattice field theory calculations investigating the confinement transition of SU(4) stealth dark matter and the possibility that this early-universe transition may produce an observable stochastic background of gravitational waves.  See the README for further information.

opencc-by-4.0Jun 2021View details →

Supplementary Data: "Dark matter, fine-tuning and mu(g-2) in the pMSSM" (arxiv 2104.03245)

The data consists of the output files of all the runs that were done. The files are organized in iterations, according to the iteration they were produced in. The map name within one iteration is labeled by the "Barbieri-Giudice fine-tuning measure"_"Electroweak fine-tuning measure"_"time stamp".  The files that are contained include: <ul> <li> spheno_in.dat, spheno_out.dat, feynhiggs14.dat, slhafile.dat, slhafileGUT.dat, susyhit.dat: in and outputs of the spectrum generators that were used. The slhafile.dat is obtained from softsusy with inputs defined at the SUSY scale, fed to FeynHiggs (produces the feynhiggs14.dat file) and then fed to SUSYHIT to produce susyhit.dat. The slhafileGUT.dat is the same spectrum but the input parameters are run to the GUT scale (defined by the scale where the unification of the coupling constants happens).The susyhit.dat file is used as input for prospino, micromegas, SUSY-AI, the fine-tuning calculation, superiso, gm2calc and ddcalc. The spheno files are used to cross-check our spectra, but not used in the results of the paper; </li> <li> DDcalcv2.out: output of the DDcalc program; </li> <li> Micromegas5_2_1.out, micro.out: micromegas output (version 5.2.1 and version 5.0.8); </li> <li> Prospino.dat: output containing the cross sections for chi1pm chi1pm, chi1pm, neu2, and slepton slepton production; </li> <li> SUSY_AI.dat: output from SUSY-AI; </li> <li> ft_contri.dat: electro-weak fine-tuning calculation, contains the number and the source of the dominant contribution; </li> <li> gm2_out.dat: output of GM2Calc; </li> <li> output_superiso, output_superiso_true: command-line output (output_superiso_true) and the slha-file (output_superiso). </li> </ul>   The data used in our plots is stored in a CSV file (datagm2_right_omegah2_only.csv). This contains all the files that have the right omegah^2.  This contains: <ul> <li>dir_name: name of saving directory;</li> <li>bsmumu_spheno,btaunu_spheno,bsgamma_spheno,gm2_spheno: calculations done by Spheno of Br(b_s -> mu+ mu-), Br(b->tau nu), Br(b->s \gamma), g-2_mu;</li> <li>flag_prospino.dat_valid,flag_gm2_out.dat_valid,flag_ft_contri.out_valid,flag_DDcalcv2.out_valid,flag_susyhit.dat_valid,flag_spheno_out.dat_valid,flag_micromegas5_2_1.out_valid,flag_output_superiso_true_valid,flag_SUSY_AI.dat_valid: flags to check whether the files were present in the output;</li> <li>flag_LSP: flag to check whether the LSP is the neutralino;</li> <li>sigmachan_5,sigmaSIn_5,sigmaSIp_5,sigmaSDn_5,sigmaSDp_5,sigmav_5,omegachan_5,sigmacontri_5,omegacontri_5,omegah_mic5,omega_chan_label,sigma_chan_label: micromegas 5.2.1 output of the dominant annihilation channel for sigmav, sigmaSIn, sigmaSIp, sigmaSDn, sigmaSDp, sigmav, dominant annihilation channel for omegah^2, contribution of the dominant annihilation channel for sigmav, contribution of the dominant annihilation channel for omegah^2, omegah^2, label for omegah dominant annihilation channel (used for plotting), label for sigmav dominant annihilation channel (used for plotting);</li> <li>pval_DarkSide,pval_XENON,pval_PICO,pval_CRESST: p-values for DarkSide, XENON, PICO, CRESST;</li> <li>gm2_mic5,dmunu_mic5,bsgnlo_mic5,dtaunu_mic5,bsmumu_mic5,btaunu_mic5: output of micromegas 5.2.1 for g-2, D->munu, b->s gamma, D->tau nu, B_s -> mu mu, B->tau nu;</li> <li>zdec_neutralinos_5: output of micromegas 5.2.1 for branching fraction of the invisible decay of the Z boson to neutralinos;</li> <li>dtaunu_iso,bsmumu_iso,btaunu_iso,bsmumuuntag_iso,bsgamma_iso,dmunu_iso,gm2_iso: the low-energy observables as calculated by SuperIso;</li> <li>susyaistopexcl,susyai8pval,susyaistoppval,susyaiewexcl,susyai13excl,susyaiewpval,susyai8excl,susyai13pval: p-values for the SUSY-AI exclusions;</li> <li>n12,n13,n11,n23,n14,n24,n31,n32,n21,n34,n22,n33,n41,n43,u11,u12,v22,v21,u21,v12,u22,n44,n42,v11: neutralino (n) and chargino (u,v) components of the mixing matrices;</li> <li>alpha_higgs: higgs mixing angle;</li> <li>mgluino,md2,mu1,md1,mu2,mc2,mc1,msneutrinotau,mH,mstop2,msbottom2,msbottom1,mstop1,mslepton1,mHpm,mDM,mn2,mn3,mn4,mstau1,mstau2,,msneutrinoe,mA0,mslepton2: masses of the pMSSM spectrum;</li> <li>MeL,M2,AB,M1,M3,MeR,Mq3L,MuR,MuL,SUSYSCALE,AT,MdR,ATAU,MbR,Ml3R,MA,MtR,tanb,mu,Ml3L: input parameters of the pMSSM spectrum;</li> <li>mhiggsFeyn: mass of the SM-like higgs boson as computed by FeynHiggs;</li> <li>FTEW: value for electro-weak fine-tuning;</li> <li>PandaX (2017),DarkSide 50,omegah_ddcalc,XENON1T (2018),DarkSide 20k,DARWIN,LZ,PICO-500,PICO-60 (2017): p-value outputs of DDcalc;</li> <li>gm2calc,gm2calc_unc: GM2Calc calculation for g-2 and the uncertainty;</li> <li>xsec_lRlR,xsec_c1pn2,xsec_lLlL,xsec_c1c1,xsec_c1mn2,xsec_stau1stau1: cross sections for slepton_R sleptonR, slepton_L, slepton_L, chi1pm chi1mp, chi1pm neu2, stau_1 stau_1;</li> <li>excl: number to determine whether the channel is excluded and for what reason (> 0.5 = excluded, < 0.5 = not excluded, 0.4 = sensitive to PICO-500, 0.26 = sensitive to LZ, 0.13 = sensitive to Darwin, 0.0 = not sensitive to any proposed DMDD future experiment). </li> </ul>

openother-openDec 2021View details →

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)]

Corrections to the results from O3 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".