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86 results for “dark matter”
Dark matter flow dataset 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>
Dark Matter Jets of Rotating Black Holes
<p>This dataset is associated with</p> <p>"Dark Matter Jets of Rotating Black Holes",<br> Ottavia Balducci, Stefan Hofmann, and Maximilian Koegler<br> (2022)</p> <p>It includes the python 3 files "boost-main.py", "kerrsystem.py" and "KerrSystemPlot.py"<br> which produce the raw data files "r_out_d_table.csv" and "mass_r_out_table.csv"<br> of figures 2 and 3.</p> <p>Additionally, it includes the raw data files "velocity_table.csv" which allows for determining the<br> boundaries of the velocity integrals and "boost_d_a_small.csv" with "boost_d_a_large.csv"<br> which demonstrate that the dark matter jet becomes more collimated for larger black hole spin.</p>
Supplementary figures for "Dark matter distribution in Milky Way-analog galaxies"
<p>Among the attached files, you will find:</p> <p>- All figures from the article in high-quality PDF format, ordered by name as follows: 'Figure1.pdf' corresponds to Figure 1 of the article, and so on;</p> <p>- Moment 0 (intensity), 1 (velocity), and 2 (dispersion) maps of the atomic hydrogen gas (HI) distribution for each galaxy in our sample. All moment maps were generated from our three-dimensional modeling with 3D-Barolo;</p> <p>- For each galaxy, we show the extended version of Figure 1 from the paper, which includes the intensity, velocity, and dispersion maps for the model and residual of each galaxy. For instance, 'NGC3521_kinematics.pdf' corresponds to the kinematic maps of NGC 3521;</p> <p>- Stellar distribution maps at 3.6 and 4.5 μm provided by the S4G survey. For instance, 'NGC3521.phot.1.fits' corresponds to the 3.6 μm image, while 'NGC3521.phot.2.fits' corresponds to the 4.5 μm image.</p>
Gamma rays from dark matter spikes in EAGLE simulations - IMBH mock catalogue
<p>DArk Matter SPIkes (DAMSPI) is a fully Python-based software for the analysis of dark matter spikes around Intermediate Mass Black Holes (IMBHs) in the Milky Way. It allows to extract an IMBH catalogue and their corresponding dark matter spike parameters from the EAGLE simulations in order to probe a potential gamma-ray signal from dark matter self-annihilation. </p> <p>The dataset contains the IMBH catalogue including, among others, the coordinates, mass, formation redshift and spike parameters for each individual IMBH. Each column of the catalogue is described in detail in J. Aschersleben et al. (2024). We also provide separate files for which we calculated the gamma-ray fluxes for different dark matter masses and annihilation cross sections. Lastly, we provide a catalogue of our selection of Milky Way like galaxies within EAGLE. The columns of these files are also described in J. Aschersleben et al. (2024).</p> <p>The source code to extract this dataset is publicy available here:</p> <div> <div> <pre><a href="https://doi.org/10.5281/zenodo.11488472">https://doi.org/10.5281/zenodo.11488472</a></pre> </div> </div> <h2>Description of the data files</h2> <p>The imbh_catalogue/imbh/ directory contains the following files:</p> <ol> <li>catalogue_nfw.h5</li> <li>catalogue_cored_gamma_0p3.h5</li> <li>catalogue_cored_gamma_0p9.h5</li> <li>catalogue_cored_gamma_free.h5</li> </ol> <p>They contain the IMBH catalogues, including the coordinates and dark matter spike parameters, calculated assuming the 1.) NFW profile, 2.) cored profile with a fixed core index of 0.0, 3.) cored profile with a fixed core index of 0.4 and 4.) cored profile with the core index as a free fitting parameter.</p> <p>The imbh_catalogue/flux/<channel>/<energy_threshold>/ directory contains the gamma-ray fluxes of the IMBHs for a given annihilation channel, energy threshold, and dark matter mass. E.g. the imbh_catalogue/flux/b_channel/e_th_0.1GeV/m_dm_10.0GeV.h5 file contains the IMBH fluxes assuming the b-channel, an energy threshold of 0.1 GeV and a dark matter mass of 10 GeV. The IMBH fluxes are calculated for a variety of velocity weighted annihilation cross sections. </p> <p>The imbh_catalogue/galaxy/ directory contains the mw_galaxies_catalogue_nfw.h5 file which contains our selection of Milky Way-like galaxies within EAGLE.</p> <p>The HDF files can be opened in Python with:</p> <pre><code>import pandas as pd file_path = "<path_to_file>.h5" df = pd.read_hdf(file_path, key="table") # Printing the first few rows of the DataFrame print(df.head())</code><code> </code></pre>
Distinguishing between pan assay interference compounds (PAINS) that are promiscuous or represent dark chemical matter - data set and prediction models
<p>Data sets of promiscuous PAINS (PROM_PAINS) and dark chemical matter PAINS (DCM_PAINS) are provided and support vector machine models built on the basis of original and balanced training data (see readme.txt).<br> </p>
Asymmetric dark matter may alter the evolution of very low-mass stars and brown dwarfs
<p>MESA inlists and data files for "<a href="https://ui.adsabs.harvard.edu/?#abs/2011PhRvD..84j1302Z">Asymmetric dark matter may alter the evolution of very low-mass stars and brown dwarfs</a>"</p>
First Results from ABRACADABRA-10 cm: A Search for Sub-$\mu$eV Axion Dark Matter — 2018 Data
<p>Extracted limits from the Axion Dark Matter search associated with the article: <em>First Results from ABRACADABRA-10 cm: A Search for Sub-μeV Axion Dark Matter </em>(To be published in Phys. Rev. Lett. 2019)</p>
Galactic Diffuse Emission Spectrum between 0.5 and 8.0 MeV and between 30 keV and 8 MeV for the search of PBH and Light Dark Matter
<p>Spectra and response files of the soft gamma-ray spectrum from Inverse Compton scattering in the Milky Way between 0.5 and 8.0 MeV, measured with INTEGRAL/SPI. The fluxes have been extracted with spimodfit assuming GALPROP v56 models according to Voyager 1, AMS-02, and Fermi/LAT data, plus different variants to change the interstellar radiation field, the diffusion properties, or the cosmic-ray halo size (<a href="https://ui.adsabs.harvard.edu/abs/2022A%26A...660A.130S/abstract">Siegert et al., 2022</a>). The respective galdef files are included. In particular:</p> <ul> <li>voyager: propagation model of Bisschoff et al. (2019), Table 2</li> <li>voyager_Dg10001: <span class="math-tex">\(\delta_1 = 0\)</span></li> <li>voyager_Dg1Dg205: <span class="math-tex">\(\delta_1 = \delta_2 = 0.5\)</span></li> <li>voyager_ISRFopt10: factor 10 stronger optical ISFR</li> <li>voyager_z8kpc_KL: thick halo with 8 kpc half-thickness</li> </ul> <p>In addition, there are spectra from 30 keV to 8 MeV (spec_0030-8000keV_<name>.fits, <a href="https://ui.adsabs.harvard.edu/abs/2022PhRvD.106b3030B/abstract">Berteaud et al., 2022</a>), separated into:</p> <ul> <li>CV: spectrum of unresolved point sources in the Galactic plane, mostly cataclysmic variables (strong systematics)</li> <li>IC: spectrum of Inverse Compton scattering of cosmic ray GeV electrons (see above)</li> <li>positronium: spectrum of positronium decay (annihilation of electrons with positrons after intermediate bound state)</li> <li>NFW: spectrum of dark matter contributions that would follow a Navarro Frenk White density profile (mostly consistent with zero)</li> <li>total: spectrum of the above combined</li> </ul> <p>The notebooks for dark matter related bounds when using this data as well as auxiliary files to create the templates are given as (<a href="https://ui.adsabs.harvard.edu/abs/2022PhRvD.106b3030B/abstract">Berteaud et al., 2022</a>, <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.4167C/abstract">Calore et al., 2023</a>):</p> <ul> <li>DM_fsr.ipynb: Notebook for final state radiation bounds</li> <li>DM_fsr.py: astromodels function for final state radiation spectrum</li> <li>DM_2gamma.ipynb: Notebook for gamma gamma final state bounds</li> <li>DM_2gamma.py: astromodels function for gamma gamma spectrum</li> <li>Positronium_Spectrum.py: astromodels function for Positonium spectrum</li> <li>DM_pbh.py: script for obtaining PBH bounds</li> <li>diff_@@.csv: spectra for PBH evaporation</li> <li>total_spectrum.fits.gz: total Galactic spectrum</li> <li>NFW_spectrum.fits.gz: NFW-only spectrum</li> </ul>
QCD Uncertainties in Particle Spectra from Dark Matter Annihilation (updated data can be found in GitHub: https://github.com/ajueid/qcd-dm.github.io.git)
<p>************************************************************************************************</p> <p>QCD Uncertainties on Particle Spectra from Dark Matter Annihilation</p> <p><strong>Please check the updated data at GitHub: https://github.com/ajueid/qcd-dm.github.io.git</strong></p> <p>Authors: Simone Amoroso, Sascha Caron, Adil Jueid, Roberto Ruiz de Austri, and Peter Skands</p> <p>If you use these tables, please cite:</p> <p>S. Amoroso et al. arXiv: 1812.07424 [hep-ph], JCAP05(2019)007</p> <p>************************************************************************************************</p> <p>We provide the spectra of stable particles in dark matter annihilation, in the galactic region or beyond, in a tabulated form using PYTHIA8 version 8235. In addition to the central prediction, we estimate for the first time the QCD uncertainties both due to hadronization as well as to showering. The uncertainties on the spectra are provided in separate tables. A wide range of dark matter masses from 10 GeV to 100 TeV is covered. We consider 11 primary annihilation channels:</p> <p>DM DM -> e+e-, mu+ mu-, tau tau, qq (q=u,d,s), cc, bb, tt, WW, ZZ, gg, and hh.</p> <p>Each file contains 13 columns: the dark matter mass, the fraction x -- defined as the kinetic energy of the particle divided by the DM mass -- in the logarithmic scale, and dN/dLog_10(x) for 11 primary channels. The provided tables correspond to the dN/dLog_10(x) of Standard Model stable particles, i.e. of photons, positrons, electron anti-neutrinos, muon anti-neutrinos and tau anti-neutrinos.</p> <p>The work on the spectra of anti-protons is ongoing (please come back soon). </p> <p>For each particle species, we provide twelve tables which can be found in zip format. The notation of the different tables is given below:</p> <p> 1) The table corresponding to the central prediction for the spectra is denoted by 'AtProduction-Hadronization1-$TYPE.dat' with $TYPE=Nuel, Numu, Nuta, Ga which refers to the three flavours of neutrinos, and photons respectively.</p> <p> 2) There are nine tables corresponding to the different variations of the light quark fragmentation function's parameters. These tables are denoted by 'AtProduction-Hadronization$h-$TYPE.dat' with h=2,..,10.</p> <p> 3) The particle spectra corresponding to the variations of the shower evolution scale (mu_R) are denoted by 'AtProduction-Shower-Var$s-$TYPE.dat' with s=1,2 corresponds to 1/2 mu_R and 2 mu_R. </p> <p><em><strong>IMPORTANT:</strong></em></p> <p> i) Uncertainty on the spectra, from hadronization, is obtained from the envelope of all the variations (including the central prediction).</p> <p> ii) In the variations of the parton shower evolution scale, the parameters of the hadronization function are fixed to their central value.</p> <p> iii) In principle, showering uncertainties are uncorrelated to hadronization uncertainties. To obtain the full uncertainty, one might combine those uncertainties in quadrature.</p> <p><em><strong>If you use the data on the site, please cite:</strong></em></p> <p>Simone Amoroso, Sascha Caron, Adil Jueid, Roberto Ruiz de Austri, Peter Skands, "Estimating QCD uncertainties in Monte Carlo event generators for gamma-ray dark matter searches," <strong>JCAP 05 (2019) 007</strong>, arXiv: 1812.07424.</p> <p><em><strong>In addition, if you use the data corresponding to shower uncertainties, please cite:</strong></em></p> <p>S. Mrenna and P. Skands, "Automated Parton-Shower Variations in Pythia 8,'' <strong>Phys. Rev. D 94 (2016) no.7</strong>, 074005, arXiv:1605.08352 [hep-ph].</p> <p><em><strong>Finally, please cite the paper of M. Cirelli et al. if you use their data for comparison or other tasks:</strong></em></p> <p>M.Cirelli, G.Corcella, A.Hektor, G.Hütsi, M.Kadastik, P.Panci, M.Raidal, F.Sala, A.Strumia, "PPPC 4 DM ID: A Poor Particle Physicist Cookbook for Dark Matter Indirect Detection'', <strong>JCAP 1103 (2011) 051</strong>, arXiv 1012.4515, Erratum: <strong>JCAP 1210 (2012) E01</strong>.</p> <p>Contact: <em>Adil Jueid</em> <adil.hep@gmail.com></p>
Dataset for paper "Decaying dark matter signal across the Milky Way?"
<p>Summary of observations after cleaning.</p> <p>File naming: mw-xmm-obs-<region>.csv, where <region> identifies corresponding region bounds in arcmin.</p> <p>List of fields:<br> ObsId - XMM-Newton observation id<br> l - galactic longitude, degrees<br> b - galactic latitude, degrees<br> off-center - an angular distance of the instrument pointing direction from the Galactic center, arcmin<br> Exposure - cleaned exposure of the MOS1/MOS2/PN cameras, ksec<br> FoV - spectra extraction regions of the MOS1/MOS2/PN spectra (field-of-view), arcmin^2</p>
Probing Dark Matter with Disappearing Tracks at the LHC
<p>This data set includes:</p> <p>1. Tables with efficiencies and cross section limits from disappearing charged tracks in ( life time - DM mass) plane for </p> <p>i2HDM (Inert Doublet Model), MFDM (Minimal Fermion DM model) and VTDM (Vector Triplet DM model).</p> <p>These are *_efficiency_and_xs_limit.dat files</p> <p>2. Tree-level cross sections VS DM mass for Pair DM production: D+D- and D+/- D</p> <p>These are * _prod_xs.dat files.</p> <p> </p> <p>See details in https://arxiv.org/pdf/2008.08581.pdf</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>
Dark Matter Annihilation and Pair Instability Supernovae
<p>Reproduction package for the paper "Dark Matter Annihilation and Pair Instability Supernovae".</p>
Supplementary animations for "Dynamical friction in self-interacting ultralight dark matter"
<p>Animations to supplement "Dynamical friction in self-interacting ultralight axion dark matter", <em>Phys.Rev.D</em> 109 (2024) 6, 063501.</p> <p>These animations are also available from <a href="https://www.youtube.com/playlist?list=PLHrf0iQS5SY6COihRVYJkz31smUyDvFwW">https://www.youtube.com/playlist?list=PLHrf0iQS5SY6COihRVYJkz31smUyDvFwW</a>.</p>
Dark Matter from Axion Strings with Adaptive Mesh Refinement - Supplementary Animations
<p>Supplementary animations to the paper "<em>Dark Matter from Axion Strings with Adaptive Mesh Refinement </em>" (arXiv:2108.05368)</p> <ul> <li><strong>2D.mp4</strong>: 2D slices through our 3D simulation. A string segment piercing the 2D plane is tracked in the right two panels. Shown is the axion field, where the string core shows up as a discontinuity, as well as the radial mode.</li> <li><strong>Axion.mp4</strong>: The evolution of the axion energy density where the string network is overlayed in yellow. The volume spans our full simulation box with periodic boundary conditions. The relative size of a Hubble volume is indicated as well by a slowly growing smaller box.</li> <li><strong>Box.mp4:</strong> We illustrate axion radiation by focusing on a string segment. The volume shown corresponds to about 1/500th of the total simulation volume. The complex behavior of the string network can be observed with its strong influence on the axion energy density.</li> <li><strong>Strings.mp4</strong>: The evolution of the string network is shown in yellow. The volume spans our full simulation box with periodic boundary conditions. The relative size of a Hubble volume is indicated as well by a slowly growing smaller box.</li> </ul>
From Images to Dark Matter: End-To-End Inference of Substructure From Hundreds of Strong Gravitational Lenses -- Data
<p>Contains the network weights and the figure files associated to the paper <a href="https://arxiv.org/abs/2203.00690">Wagner-Carena et al. 2022</a>.</p> <p>The file data.zip is meant to be downloaded in the folder notebooks/papers/2203_00690.</p> <p>The two .h5 files represent the trained model weights after 100 epochs of the diagonal model and the full model. See the paper for more details.</p> <p> </p>
Dance Your Ph.D. 2018 Physics FINALIST: Dark energy and dark matter via probabilistic data analysis
<p>Dark matter (dancer in black mask; brings dancers together) and dark energy (dancer in black mask; pulls dancers apart) are the mysterious constituents of the universe that determine the evolution of the largest structures, clusters of galaxies (dancers in tie dye), and I (dancer in gold mask) want to learn about them them by analyzing a huge set of 3D galaxy positions. The telescope (dancer in silver mask) gives me 2D images of galaxies with no distances, so I have to estimate galaxy locations with "redshift," which is correlated with distance. The exciting telescopes of the next decades will build large samples of galaxies by taking lots of superficial color data that's not informative enough to determine the redshift. One thing that tells us about dark matter and dark energy is the redshift distribution (dancer in red stripes) -- I can still learn about dark energy and dark matter if I can figure out how many galaxies have each redshift. Since I don't actually know the redshifts of any galaxies, I consider the probability of redshift (dancers in white shirts) for each galaxy based on its limited color data. However, the redshift probabilities are biased by prior beliefs (dancer in gray scarf) about physics that are transmitted by the way we derive the probabilities from the galaxy colors. If unaccounted for, the prior beliefs could cause us to draw the wrong conclusions about dark matter and dark energy, meaning we won't learn anything from all this amazing telescope data we're going to have. For my thesis, I developed a method that puts the prior beliefs in their proper place and lets the data guide the inference of the redshift distribution, using an approach called Bayesian statistics. I audition many possible redshift distributions and check how likely each is to have caused the observed galaxy colors, with the redshift probabilities as the judges; this procedure is called Markov chain Monte Carlo (MCMC) sampling, and it leads to an ensemble of possible redshift distributions each with an associated probability of having produced the observed galaxy colors. By doing the math carefully and correctly, we will learn more about dark matter and dark energy even with the coarsest data!</p>
UFO model for pseudo-Nambu-Goldstone Dark Matter
<p>pseudo-Nambu-Goldstone (pNG) dark matter in a complex scalar singlet model with a softly broken global U(1) symmetry. Python3 compatible version</p>
Revisiting GeV-scale annihilating dark matter with the AMS-02 positron fraction
<p>We include our dark matter fluxes in their pre-fitted format for the entire set. That is in connection to our paper titled "Revisiting GeV-scale annihilating dark matter with the AMS-02 positron fraction"</p>
Likelihoods for the CTA sensitivity to a dark matter line signal from the Galactic centre (S. Abe et al., 2024])
<p>We present likelihoods to estimate upper limits and detection prospects for monochromatic gamma-ray signals from DM pair-annihilation in the Galactic centre, based on the Cherenkov Telescope Array (CTA) consortium publication "Dark Matter Line Searches with the Cherenkov Telescope Array" by S. Abe et al. [arxiv:<a href="https://arxiv.org/abs/2403.04857" target="_blank" rel="noopener noreferrer">2403.04857</a>]. These likelihoods are based on the analysis benchmark assumptions summarized in Table 3 of that publication. In particular, they assume an Einasto profile for the DM density distribution and 500 hrs of Galactic centre observations. We further supply likelihoods for the same analysis benchmark assumptions, but for an Einasto profile with an inner core of size 1 kpc.</p> <p>As indicated by the filenames, the four files contain likelihoods for setting limits and claiming discovery, respectively. For more technical details, see the headers of these files. </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.