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118 results for “COSMIC”
Detecting cosmic voids via maps of geometric optics parameters
<ul> <li>lensing-ddbb4ac.pdf - research data in pdf format</li> <li>void_matches*.dat - plain text results files corresponding to Table 3 and Figures 2, 4, 6, 8.</li> <li>lensing-ddbb4ac-journal.tar.gz - source package for producing the article pdf, together with the reproducibility package, but without the git history; appropriate for ArXiv</li> <li>lensing-ddbb4ac-git.bundle - git source package that can be unbundled with 'git clone lensing-e4f7af0-git.bundle' and used for reproducibility: to download data, do calculations, analyse them, plot them and produce the research data pdf</li> <li>software-ddbb4ac.tar.gz - this should contain all the software, apart from a minimal POSIX-compatible system and LaTeX packages, needed for compiling and installing the software used in producing this work</li> <li>lensing-ddbb4ac-snapshot.tar.gz - source files of the project; these should be enough, provided that external software packages can be downloaded, to reproduce the full project</li> </ul> <p>The authors grant a perpetual, non-exclusive licence to distribute this pdf preprint.</p> <p>All the other materials here are free-licensed, as stated in the individual files and packages.</p>
SILC cosmic microwave background (CMB) maps R1 of Planck PR2 data
<p>Clean maps of the CMB temperature anisotropies (as measured by Planck; public data release PR2) constructed with a novel internal linear combination (ILC) algorithm using directional, scale-discretised wavelets – Scale-discretised, directional wavelet ILC or SILC.</p>
Spin-SILC cosmic microwave background (CMB) polarisation maps R1 of Planck PR2 data
<p>Clean maps of the CMB linear polarisation <em>E</em>/<em>B</em> and Stokes <em>Q</em>/<em>U</em> fields (as measured by Planck; public data release PR2). They are constructed with a novel internal linear combination (ILC) algorithm using spin, directional, scale-discretised wavelets – Spin, Scale-discretised, directional wavelet ILC or Spin-SILC.</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>
Cosmic Pi North Pole Dataset 2024
<p>This raw dataset was gathered using a pair of Cosmic Pi detectors carried by the Transglobal Car Expedition across North America, to the North Pole and back to Greenland. To our knowledge this is the first ground level measurement of muons at the North Pole, beating the record previously set by the PolarquEEEst mission, which sailed up to a latitude of 82◦07′ (see https://cds.cern.ch/record/2743921/files/ncc11933(1).pdf for details).</p> <p>The dataset comprises a portable Influxdb 1.8 export, with several sub datasets. </p> <p>We have the following data stored in the CosmicPiV1.8.1 measurement table:</p> <ul> <li>measurement='CosmicPiV1.8.1',</li> <li>DeviceID=cosmicdict['tags']['id'], Unit ID</li> <li>latitude=cosmicdict['fields']['lat'], Latitude</li> <li>longitude=cosmicdict['fields']['lon'], Longitude</li> <li>Temp=cosmicdict['fields']['Temp'], Temperature in C</li> <li>Hum=cosmicdict['fields']['Hum'], Relative Humidity in %</li> <li>Accelx=cosmicdict['fields']['Accelx'], Acceleration, X axis in m/s/s</li> <li>Accely=cosmicdict['fields']['Accely'], Acceleration, Y axis in m/s/s</li> <li>Accelz=cosmicdict['fields']['Accelz'], Acceleration, Z axis in m/s/s</li> <li>Magx=cosmicdict['fields']['Magx'], Magnetic field, X axis in Gauss</li> <li>Magy=cosmicdict['fields']['Magy'], Magnetic field, Y axis in Gauss</li> <li>Magz=cosmicdict['fields']['Magz'], Magnetic field, Z axis in Gauss</li> <li>Pressx=cosmicdict['fields']['Press'], Atmospheric pressure in mbar</li> <li>Altx=cosmicdict['fields']['Alt'], Altitude in meters</li> <li>timestamp=cosmicdict['time'] Time</li> </ul> <p>And we also have the frequency data (how many cosmic rays detected per second) here:</p> <ul> <li>measurement='CosmicPiV1.8.1_freq',</li> <li>DeviceID=cosmicdict['tags']['id'], Unit ID</li> <li>geohash_pos="""+cosmicdict['fields']['geohash']+""", The geohash, a short code that represents lat & lon</li> <li>event_count=cosmicdict['fields']['event_count'], The number of events in this second</li> <li>timestamp=cosmicdict['time']) Time</li> </ul> <p>Some basic analysis code is presented here: https://github.com/CosmicPi/TGCEWorkshop</p>
Dataset for Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator
<p>The dataset contains all gathered data from the experiment from Wednesday, March 16, 2022 11:58:41.929 AM UTC+0 (1647431921929) until Sunday, April 3, 2022 1:08:35.353 PM UTC+0 (1648991315353). The experiment was executed during physical presence within the Arctic Circle in Tromsø, Norway 69° 40' 53.117'' N 18° 58' 36.027'' E at 35m elevation above sea level. The dataset was gathered with a prototype [1] based on the CREDO android application [2]. The main research is to use Ultra High Energy Cosmic Rays (UHECR) as an entropy source for a Random Bit Generator (RBG). </p> <p>The associated publication will probably have the title "Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator"</p> <p>In order to reproduce the results the SQLite3 database "mrng_arctic_experiment_2022.db" is needed. To get the visual representations of the detections use "image_decoding_and_codesnippets.py" to generate the cleaned (414 detections / ~15MB) or the uncleaned (5567 detections / ~195 MB) dataset. The compressed folder "raw_data_incl_space_weather.7z" contains all raw data as gathered with the MRNG prototype, unprocessed, uncleaned, and unmerged. </p> <p> </p> <p>[1] https://github.com/StefanKutschera/mrng-prototype, visited on 27.03.2023</p> <p>[2] https://github.com/credo-science/credo-detector-android, visited on 27.03.2023</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>
The Cosmic-Ray Energy Spectrum
<p>This plot shows a compilation of the cosmic-ray energy spectrum measured by several experiments (after 2000).</p> <p>References are listed in a dedicated GitHub <a href="https://github.com/carmeloevoli/The_CR_Spectrum">repository</a>.</p>
SDSS-IV Cosmic Web Catalog
<p>This repository contains the cosmic web catalog data released in the paper "Cosmic Web Catalog on SDSS-IV Data with SCONCE" (preparing).</p> <p>The catalog is constructed on the SDSS-IV galaxies and quasars (QSO) using our proposed Directional Subspace Constrained Mean Shift (DirSCMS) algorithm. We release both the cosmic filaments and local modes (i.e., local maxima of the estimated galaxy/QSO density field, which serves as candidates of galaxy clusters) within 325 thin redshift slices, each of which spans 20Mpc under the Planck15 cosmology. The entire catalog covers the redshift range from <span class="math-tex">\(z=0\)</span> to <span class="math-tex">\(z=3\)</span>.</p> <p> 1. "<strong>Cosmic_filaments_2D_DirSCMS_new1</strong>": The file contains some discrete realizations of the estimated cosmic filaments in some particular redshift slices. The meaning of each column in the file is described as follows:</p> <ul> <li><strong>RA</strong> -- right ascension.</li> <li><strong>DEC</strong> -- declination.</li> <li><strong>z_low</strong> -- lower limit of the redshift slice.</li> <li><strong>z_high</strong> -- upper limit of the redshift slice.</li> <li><strong>comov_dist_low</strong> -- lower limit of the comoving distance in the redshift slice under the Planck15 cosmology.</li> <li><strong>comov_dist_high</strong> -- upper limit of the comoving distance in the redshift slice under the Planck15 cosmology.</li> <li><strong>bw</strong> -- smoothing bandwidth parameter for the DirSCMS algorithm in the redshift slice.</li> <li><strong>unc_meas</strong> -- uncertainty measure of the filamentary point by the nonparametric bootstrap techinque.</li> <li><strong>density</strong> -- (proportional) estimated galaxy/QSO density value at the filamentary point.</li> <li><strong>grad_Dir1</strong> -- (Riemannian) gradient of the estimated density field (first direction).</li> <li><strong>grad_Dir2</strong> -- (Riemannian) gradient of the estimated density field (second direction).</li> <li><strong>grad_Dir3</strong> -- (Riemannian) gradient of the estimated density field (third direction).</li> <li><strong>knot_label</strong> -- indicator of whether the filamentary point is a knot (i.e., the intersection of several filaments) or not.</li> </ul> <p> 2. "<strong>Cosmic_local_modes_2D_DirMS_unique1</strong>": The file contains some discrete realizations of the estimated local modes in some particular redshift slices. The columns are subsumed by the ones in the cosmic filament file and has been described above.</p> <p><em>Additional notes: We provide both the "csv" and "fits" format for each of the above file.</em></p> <p> </p> <p>Please cite the paper when using the data in this repository.</p> <p>► Pure catalog data:</p> <p>[1] <strong>Cosmic Web Catalog on SDSS-IV Data with SCONCE</strong>. (In preparation)</p> <p>►Methodology:</p> <p>[1] Yikun Zhang, Rafael S. de Souza, and Yen-Chi Chen (2022). <strong>SCONCE: A Cosmic Web Finder for Spherical and Conic Geometries</strong>. <em>arXiv preprint arXiv:2207.07001</em></p> <p>[2] Yikun Zhang and Yen-Chi Chen (2022) <strong>Linear Convergence of the Subspace Constrained Mean Shift Algorithm: From Euclidean to Directional Data</strong>. <em>Information and Inference: A Journal of the IMA</em>, iaac005, <a href="https://doi.org/10.1093/imaiai/iaac005">https://doi.org/10.1093/imaiai/iaac005</a></p> <p>[3] Yikun Zhang and Yen-Chi Chen (2021) <strong>Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data</strong>. <em>Journal of Machine Learning Research</em> <strong>22</strong>(154): 1-92.</p>
Supporting Information for "An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities"
<p>This supporting information provides the Data Set S1 used to produce Fig. 6 in <strong>"An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities"</strong> (Gieseler et al., 2017). It can be used to calculate the rigidity-dependent solar modulation potential <span class="math-tex">\(\phi(P)\)</span> for monthly intervals from 1973-2017 following Eq. 10 in Gieseler et al. (2017).</p> <p>If you use this data, please refer to and cite <strong>BOTH</strong> following publications:</p> <ul> <li>Gieseler, J., B. Heber, and K. Herbst, <em>An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities</em>, J. Geophys. Res., 2017 (doi:10.1002/2017JA024763).</li> <li>Usoskin, I. G., G. A. Bazilevskaya, and G. A. Kovaltsov, <em>Solar modulation parameter for cosmic rays since 1936 reconstructed from ground-based neutron monitors and ionization chambers</em>, J. Geophys. Res., 2011 (doi:10.1029/2010JA016105).</li> </ul> <p>This data set contains the solar modulation potential values in MV for monthly intervals from 1973-2017 derived from the proton proxies IMP-8 He and ACE/CRIS C (Phi_pp), and from Usoskin et al. (2011) as provided by http://cosmicrays.oulu.fi/phi/phi.html (Phi_Uso11). The uncertainties of Phi_pp are given in column 4, those of Phi_Uso11 are 26 MV for the observed period. The LIS used to calculate the modulation potentials is that from Burger et al. (2000) as given by Usoskin et al. (2005).</p> <p>Column 1: Fractional year (start of interval)<br> Column 2: Month<br> Column 3: Phi_pp /MV<br> Column 4: Uncertainty of Phi_pp /MV<br> Column 5: Phi_Uso11 /MV</p> <p>Data also available at http://www.ieap.uni-kiel.de/et/ag-heber/cosmicrays</p>
Cosmic-CoNN LCO CR Dataset
<p>Cosmic-CoNN Las Cumbres Observatory CR dataset<br> ========================</p> <p>Cosmic rays in the Las Cumbres Observatory (LCO) CR dataset are labeled accurately and consistently across many diverse observations from various instruments. To the best of our knowledge, this is the largest cosmic ray dataset that identifies CRs in science images across various ground-based instruments. It consists of over 4,500 observations from Las Cumbres Observatory global telescope network's 23 instruments. Each sample in our dataset is a multi-extension FITS file, including three images, three corresponding CR masks, and three ignore masks. </p> <p>Usage: https://github.com/cy-xu/cosmic-conn</p> <p>For questions regarding the dataset, please contact cxu@ucsb.edu.</p>
Migrating Tides in the Stratosphere from COSMIC Radio Occultation Data
<p>These analyses of the migrating tides in temperature, microwave refractivity, and geopotential in the Earth’s stratosphere are analyzed using GPS radio occultation (RO) data obtained by the COSMIC constellation of six satellites in orbit planes separated by 30° in ascending node. The tides are analyzed monthly, beginning with November 2006 and ending with December 2016. The radio occultation retrievals used as input to the analyses are obtained from the Climate Data Record v1 of the EUMETSAT Radio Occultation Meteorology Satellite Application Facility (ROM SAF; romsaf.org). The reference model that is used for the sake of comparison are the 3-, 6-, 9-, and 12-hr forecasts of the ERA-Interim reanalysis project. </p>
Dataset from "More accurate gravitational wave backgrounds from cosmic strings"
<p>This file contains data tables representing gravitational wave backgrounds (GWBs) produced by Nambu-Goto cosmic strings evolved under numerical gravitational backreaction. The GWBs were produced using the methodology of "More accurate gravitational wave backgrounds from cosmic strings" [to appear], by the same authors as this dataset.</p> <p>The file is organized in three columns:</p> <ol> <li>The base-10 logarithm of the string coupling to gravity, G\mu. The range is from -8 to -22 in steps of -0.1.</li> <li>The frequency in Hz, f. The range is from 10^(-12) Hz to 10^5 Hz in multiplicative steps of 10^(0.01).</li> <li>The critical energy density fraction in gravitational waves scaled by the dimensionless Hubble constant squared, \Omega_{gw} h^2.</li> </ol>
Data Release of Cosmic evolution of the incidence of Active Galactic Nuclei in massive clusters: Simulations versus observations
<p>Dataset of the paper "Cosmic evolution of the incidence of Active Galactic Nuclei in massive clusters: Simulations versus observations".</p> <p> </p> <p>All the necessary code to deal with these data can be found in: https://github.com/IvanMuro/agn_frac_data_release</p>
Magnetic field data for "Cosmic Rays in Intermittent Magnetic Fields" (magnetic fields produced by the small-scale dynamo)
<p>Magnetic field data for the kinematic dynamo generated magnetic fields (KS) employed in the cosmic ray test particle simulations of Shukurov et al. 2017, <em>ApJL</em>, <strong>839</strong>, L16 [<a href="https://doi.org/10.3847/2041-8213/aa6aa6">https://doi.org/10.3847/2041-8213/aa6aa6</a>]. One dataset was also used in "Relative distribution of cosmic rays and magnetic fields", Seta et al. 2018, <em>MNRAS</em>, <strong>473 </strong>(4), 4544-4557 [<a href="https://doi.org/10.1093/mnras/stx2606">https://doi.org/10.1093/mnras/stx2606</a>]. These studies investigated the effects of magnetic field structure on charged test particle transport and trapping. See the README file for more details.</p> <p> </p>
Cosmic Muon Images open classification data
<p>This is a reduced, anonymised dataset containing the classifications made in the Cosmic Muon Images demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.</p>
Diffuse Emission of High-Energy Neutrinos from a Global Fit to Cosmic Rays
<p>Model of diffuse emission of high-energy neutrinos from a global fit of cosmic rays and model of high-energy neutrino emission from unresolved pulsar-powered sources.</p> <p>The maps presented in the form of <em>HEALPix </em>maps (Gorski et al 2005, ApJ, 622, 759) of per-flavor intensity in units of GeV<sup>-1</sup> cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> at 50 logarithmically spaced energies between 10 GeV and 10<sup>8</sup> GeV. We use a value of NSIDE=256 and the RING binning scheme.</p> <p>We here make available our fiducial model, which is calculated assuming the <em>Ferrière 2001</em> cosmic ray source distribution, the <em>AAfrag</em> hadronic production cross sections and the <em>GALPROP</em> gas maps. We calculated the emission from unresolved sources following Vecchiotti et al. 2022, ApJ, 928, 19.</p> <p>In Version 2 of this dataset, we also make available the local cosmic ray fluxes of our fiducial model obtained from a global fit to cosmic ray data together with the corresponding 68% and 95% uncertainty bands. These are shown in figure 6 of <a href="https://arxiv.org/abs/2211.15607">arXiv:2211.15607</a>. The nuclear fluxes are in (GeV/n)<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup>, the fluxes of electrons and positrons are in GeV<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> . The fluxes are local interstellar fluxes without solar modulation.</p> <p>In Version 3 of this dataset, we add the fiducial diffuse gamma ray model calculated assuming the <em>Ferrière 2001</em> cosmic ray source distribution, the <em>AAfrag</em> hadronic production cross sections as well as the <em>GALPROP</em> gas maps and ISRF model. We separately make available 3 maps: The hadronic emission on neutral atomic gas, the hadronic emission on molecular gas and the leptonic emission from Inverse Compton Scattering. </p> <p>Similar to the dataset of the fiducial neutrino model, the maps are presented in the form of <em>HEALPix </em>maps (Gorski et al 2005, ApJ, 622, 759) in units of GeV<sup>-1</sup> cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup>. We use a value of NSIDE=256 and the RING binning scheme. For the hadronic maps, the intensity is given at 50 logarithmically spaced energies between 10 GeV and 10<sup>8</sup> GeV. For the leptonic maps from Inverse Compton Scattering, the intensity is given at 48 logarithmically spaced energies between 1 GeV and 10<sup>6</sup> GeV.</p> <p>The structure of the files is somewhat different from the file containing the fiducial neutrino model. This is to allow for easy use of the gamma ray maps with the <em>gammapy</em> package (Deil et al. 2017, <a href="https://arxiv.org/abs/1709.01751"> arXiv:1709.01751</a>).</p> <p>Also available in Version 3 are the full spatio-spectral cosmic ray distributions in the Milky Way as predicted by our fiducial model. The nuclear fluxes are given for each species in (GeV/n)<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> at 63 energies between 1 GeV and 10<sup>9</sup> GeV. The leptonic fluxes are given for each species in GeV<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> at 36 energies between 1 GeV and 10<sup>5</sup> GeV. </p> <p>All fluxes are given on a spatial grid at 81 galactocentric radii from 0 kpc to 20 kpc and 61 distances perpendicular to the galactic plane between -6 kpc and 6 kpc.</p> <p>Finally, a word of caution about the extra component of cosmic ray leptons included in our model: This component is contained in the last <em>HDUnit</em> of the <em>fits</em> file containing the leptonic cosmic ray distributions. It is there denoted as a flux of electrons. It must, however, also be added to the flux of positrons to achieve correct results.</p> <p>Please refer to <a href="https://arxiv.org/abs/2211.15607">arXiv:2211.15607</a> for further details.</p> <p>When using these models in your research work, please refer to this Zenodo dataset and the publication.</p>
Cosmic Explorer MPSAC Dataset
<p>This dataset contains the compact binary populations that were used in the Cosmic Explorer MPSAC White paper<sup>1</sup> (submitted to the NSF MPSAC ngGW Subcommittee) and the accompanying technical paper<sup>2</sup>. </p> <p><strong>Contents:</strong></p> <p>1. 1-year populations for binary black hole (BBH), binary neutron star (BNS), neutron star-black hole (NSBH), intermediate mass binary black hole (IMBBH), Population III (Pop 3) binary black holes and primordial black hole (PBH) mergers. It also contains the SNRs and measurement errors on intrinsic and extrinsic parameters calculated using gwbench<sup>3</sup>.</p> <p>2. 1/4-year sub-population of BNS mergers for which errors on tidal parameters were calculated.</p> <p>3. An ipython notebook (instructions.ipynb) that shows how the data can be used.</p> <p><strong>References:</strong></p> <p>1. Evans, Matthew <em>et al.</em> Cosmic Explorer: A Submission to the NSF MPSAC ngGW Subcommittee (2023). arXiv: 2306.13745 [gr-qc].</p> <p>2. Gupta, Ish <em>et al. </em>Characterizing Gravitational Wave Detector Networks: From A<sup>#</sup> to Cosmic Explorer (2023). <em>In preparation.</em></p> <p>3. Borhanian, Ssohrab. GWBENCH: a novel Fisher information package for gravitational-wave benchmarking. Class. Quant. Grav. 38, 175014 (2021). arXiv: 2010.15202 [gr-qc]. </p>
Data Release: "LIGO-Virgo-KAGRA's Oldest Black Holes: Probing star formation at cosmic noon with GWTC-3"
<p>This repository contains the data behind the figures presented in v2 of "LIGO-Virgo-KAGRA's Oldest Black Holes: Probing star formation at cosmic noon with GWTC-3" (<a href="https://ui.adsabs.harvard.edu/link_gateway/2023arXiv230715824F/arxiv:2307.15824">arXiv:2307.15824</a>), to appear in ApJL.</p><p>The csv files (in Output.zip) and the h5 files contain the data products. The three Jupyter notebooks include code for plotting the figures and calculating the summary statistics that appear in the paper. </p><p> </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>
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.