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142 results for “gravitational waves”
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>
Constraining scalar-tensor theories by neutron star-balck hole gravitational wave events
<p>This data release corresponds to the paper "Constraining scalar-tensor theories by neutron star-balck hole gravitational wave events" (<a href="https://arxiv.org/abs/2105.13644">arXiv:2105.13644</a>). In this paper, we consider three specific models of scalar-tensor theories, including the Brans-Dicke theory (BD), the theory with scalarization phenomena proposed by Damour and Esposito-Far\`{e}se (DEF), and Screened Modified Gravity (SMG). From all 4 possible NSBH events so far, we use two of them to place the constraints. The other two are excluded in this work due to the possible unphysical deviations. Four equations of state (EoSs), <em>sly</em>, <em>alf2</em>, <em>H4</em> and <em>mpa1</em>, are used to derive the scalar charges of neutron stars for BD and DEF. The constraints are obtained by performing the full Bayesian inference with the help of the open source software <a href="https://git.ligo.org/lscsoft/bilby">Bilby</a>.</p> <p>This dataset contains all posterior samples of the runs discussed in the paper. The models and EoSs can be read form the filenames for the runs of BD and DEF. The files of "<em>*_half_dipole.json</em>" correspond to the runs for constraining the dipole radiation without considering specific model parameters. All files are JSON format which is the default output format of <a href="https://git.ligo.org/lscsoft/bilby">Bilby</a>. They are human readable and also can be processed or visualized by <a href="https://git.ligo.org/lscsoft/bilby">Bilby</a> or <a href="https://git.ligo.org/lscsoft/pesummary">PESummary</a> conveniently.</p>
Insights into non-axisymmetric instabilities in three-dimensional rotating supernova models with neutrino and gravitational-wave signatures
<p>Those are movies of numerical supernova models, which appear in Takiwaki, Kotake, and Foglizzo, (2021), Monthly Notices of the Royal Astronomical Society, Volume 508, Issue 1, pp.966-985</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>
Classifying the generation and formation channels of dynamically-formed gravitational-wave events
<p>This dataset contains all the simulations of dynamically-formed binaries performed with the software <a href="https://github.com/Kkritos/Rapster">rapster</a>, together with trained machine-learning classification models from (Antonelli, Kritos et al, in prep.), see <a href="https://github.com/aantonelli94/TheBHClassifier">the public codes online</a>.</p> <p>All items starting with "mergers_*" are simulations of clusters and they follow the structure reported in the documentation of <a href="https://github.com/Kkritos/Rapster">rapster</a>. The simulations differ in the choice of the hyperparameters for the distribution of the cluster mass, half-mass radius and initial spin distribution for the binaries.</p> <p>All items starting from "RFClassifier_*" are machine-learning classification models that use a Random Forest Classifier and that are trained with the simulations above. The models ending with "*_gen" predict the generation of the black holes, those with "*_form" predict their formation channels. </p> <p> </p>
Data release for paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method"
<p>This is the data release for the paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method", which is available on https://arxiv.org/abs/2306.12908.</p> <p>These results are derived from the gravitational-wave parameter-estimation results by the LIGO-Virgo-KAGRA Collaboration, released with the GWTC-1, GWTC-2, GWTC-2.1, and GWTC-3 catalogs under the following links:</p> <ul> <li> https://dcc.ligo.org/P1800370-v5/public</li> <li> https://dcc.ligo.org/P2000223-v7/public</li> <li> https://doi.org/10.5281/zenodo.6513631</li> <li> https://doi.org/10.5281/zenodo.5546663</li> </ul> <p>For the lensed-unlensed hypothesis test posterior overlap Bayes factors, we provide the following files for event pairs from within each observing run:</p> <ul> <li> blu_all_pairs_O1.txt</li> <li> blu_all_pairs_O2.txt</li> <li> blu_all_pairs_O3.txt</li> </ul> <p>In each file, the column "event_pair" contains the names of the two events from the pair sorted chronologically, the column "data_releases" contains the names of the data releases from which the posterior samples of each event were taken, the column "waveform" contains the name of the waveform model used in the parameter estimation for both sets of posteriors, and the column "log10blu" contains the log10 of the Bayes factors.</p> <p>The differences between runs for the same event pair, only including O1-O1, O2-O2, O3-O3 pairs, where at least one run gave log10blu>0, are also given in the file "blu_differences_pairs_with_log10blu_pos.txt". The column "event_pair" contains the event pairs, the columns "waveform_{1,2}" contain the names of the waveform models used in the parameter estimation for both sets of posteriors, the columns "data_releases_{1,2}" contain the the data releases from which the posterior samples of each event were taken, the columns "log10blu_{1,2}" contain the log10 Bayes factors, and the column "difference" contains the difference between "log10blu_1" and "log10blu_2".</p> <p>We also provide the following files corresponding to the appendix of the paper, analyzing overlaps between posterior samples for individual events:</p> <ul> <li> overlap_different_runs.txt</li> <li> overlap_same_run.txt</li> <li> rescaled_difference_single_event.txt</li> </ul> <p>The file "overlap_different_runs.txt" contains Bayes factors for a single event, but comparing the posteriors from different runs. The file "overlap_same_run.txt" contains Bayes factors for the overlap of a single run on a single event with itself. The file "rescaled_difference_single_event.txt" contains the difference between the results contained in the file overlap_different_runs.txt and the results in overlap_same_run.txt, taking the ones that produce the biggest difference, as per equation (A.1) in the paper.</p> <p>In these files, the column "event_name" is the name of the event, the column "data_release" or "data_releases" contains the name(s) of the data release(s) from which the posterior samples of each run were taken, the column "waveform" or "waveform_pair" contains the name(s) of the waveform model(s) used, and the column "log10blu" is the log10 Bayes factor obtained. In the file "rescaled_difference_single_event.txt", the columns "max_run_waveform" and "max_run_data_release" identify an entry from the "overlap_same_run.txt" file from which we use the "log10blu" to compute the value listed in the "difference" column using equation (A.1).<br> </p>
New Sensitivity Curves for Gravitational-Wave Experiments
<p>Supplemental material for the paper of the same name, arXiv:2002.04615 [hep-ph], consisting of all (1) strain noise power spectra, (2) power-law-integrated sensitivities, and (3) peak-integrated sensitivities presented in this paper.</p>
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>
Constraining properties of the next nearby core-collapse supernova with multi-messenger signals: gravitational wave frequency fits
<p>1D FLASH simulations with STIR, for alpha_lambda = 1.23, 1.25, and 1.27. Run with SFHo EOS, M1 with 12 energy groups.</p> <p>For more information on these simulations, see Warren, Couch, O'Connor, & Morozova (arXiv:1912.03328) and Couch, Warren, & O'Connor (2020).</p> <p>Includes fit to the gravitational wave peak frequency versus time post-bounce, for a functional fit of the form f = A*sqrt(t) + B*t + C, where the frequency f is in Hz and the time t is in seconds. The columns are: progenitor mass [M_sun], fit coefficient A, fit coefficient B, fit coefficient C, and the R^2 of the fit.</p>
Stealth dark matter confinement transition and gravitational waves --- data release
<p>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.</p> <p> </p>
Equation of State Effects on Gravitational Waves from Rotating Core Collapse
<p>Gravitational waveforms from 1824 fiducial and detailed electron capture simulations, sampled at 65535 Hz. The file is in HDF5 format, using the flags {dtype="f4",compression="gzip",shuffle=True,fletcher32=True}. Each group is contained in the "waveforms" top-level group and is named with the "A" and "omega_0" values from Equation 5 and the EOS. In each sub-group is a dataset containing timestamps in seconds (t=0 is core bounce) and a dataset containing the strain multiplied by the distance in centimeters. The values of A in kilometers, omega_0 in radians/s, and the EOS are stored as attributes of each group.</p> <p>In addition, the Ye(rho) profiles are stored in the "yeofrho" top-level group. Each sub-group is labeled by the EOS used to generate the profile.</p> <p>Finally, select reduced data is stored in the "reduced_data" top-level group. The following quantities are each stored as a 1824-element array, where elements of the same index from different datasets correspond to the same 2D simulation.</p> <p>A(km) -- differential rotation parameter in Equation 5<br> D*bounce_amplitude_1(cm) -- The minimum of the first (negative) GW strain peak, multiplied by distance.<br> D*bounce_amplitude_2(cm) -- The maximum of the second (positive) GW strain peak, multiplied by distance.<br> EOS -- the equation of state used in the simulation<br> MbarICgrav(Msun) -- gravitational mass of the inner core, averaged over time after core bounce<br> Mgrav1_IC_b(Msun) -- gravitational mass of the inner core at bounce<br> Mrest_IC_b(Msun) -- rest mass of the inner core at bounce<br> SNR(aLIGOfrom10kpc) -- signal to noise ratio of the GW signal, assuming a distance of 10kpc and aLIGO sensitivity<br> T_c_b(MeV) -- central temperature at bounce<br> Ye_c_b -- central electron fraction at bounce<br> alpha_c_b -- central lapse at bounce<br> beta1_IC_b -- ratio of rotational kinetic to gravitational potential energy of the inner core at bounce<br> fpeak(Hz) -- frequency of the post-bounce GW oscillations<br> j_IC_b() -- angular momentum of the inner core at bounce<br> omega_0(rad|s) -- initial (pre-collapse) rotation rate used in Equation 5<br> omega_max(rad|s) -- maximum rotation rate achieved outside of 5km<br> rPNSequator_b(km) -- radius of the rho=10^11 g/ccm contour along the equator at bounce<br> rPNSpole_b(km) -- radius of the rho=10^11 g/ccm contour along the pole at bounce<br> r_omega_max(km) -- radius where omega_max occurs<br> rho_c_b(g|ccm) -- central density at bounce (not time averaged)<br> rhobar_c_postbounce(g|ccm) -- central density time averaged after bounce<br> s_c_b(kB|baryon) -- central entropy at bounce<br> t_postbounce_end(s) -- time of the end of the postbounce signal (t=0 is core bounce)<br> tbounce(s) -- time of core bounce (t=0 is the beginning of the simulation)<br> </p>
Data package for paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events
<p>This is a data package accompanying the paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events".</p>
Auxiliary data release for "Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization"
<p>This release contains parameter estimation runs on synthetic injections, described in https://arxiv.org/abs/2404.02435 .</p> <p>Important note: The posterior samples provided are weighted, the weights are stored in a column named 'weights' . </p>
Time-frequency maps of gravitational waves embeded in noise
<p>This data set constains the time-frequency maps of gravitational waves embeded in noise. </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>
Data release: Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes
<p>The data required to reproduce the analyses of "Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes" (<a href="https://arxiv.org/abs/2404.03166" target="_blank" rel="noopener">arxiv:2404.03166</a>). The main inference code can be found at <a href="https://github.com/AnaryaRay1/gppop/tree/spin-dev" target="_blank" rel="noopener">https://github.com/AnaryaRay1/gppop/tree/spin-dev </a> (commit: <a href="https://github.com/AnaryaRay1/gppop/commit/ee5ffc421e2c96eeed15a0e0d3839da42b982842">ee5ffc</a>). To reproduce the analyses, follow the instructions at <a href="https://github.com/AnaryaRay1/bbh-subpopulations-scripts">https://github.com/AnaryaRay1/bbh-subpopulations-scripts</a> (commit <a href="https://github.com/AnaryaRay1/bbh-subpopulations-scripts/commit/de88f931d8c1a2cb31ad2fa9d6fdf9a5a00a3c3b">de88f93</a>). Frozen versions of these repositories that were used to generate all the results are available as part of this data release, in the files "gppop_spin_dev_ee5ffc421.tar.gz" and "bbh-subpopulations-scripts_de88f931.tar.gz" respectively.</p>
Datasets for ``Leading-order nonlinear gravitational waves from reheating magnetogeneses''
<pre>This directory contains an index.html file with links to the run directories with secondary data for Table II of the paper "Leading-order nonlinear gravitational waves from reheating magnetogeneses" by Yutong He, Axel Brandenburg, and Alberto Roper Pol. If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>
Reproduction package for "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data"
<p>This is a basic reproduction package for the paper "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data" by Gourdji et al. (2021) published in MNRAS. It describes the software and settings used to obtain the final data products of the analysis. It also includes a Jupyter notebook and required data to reproduce the tables and figures of this paper.</p>
Datasets for ``Big bang nucleosynthesis limits and relic gravitational waves detection prospects''
<pre>This directory contains an index.html file with links to the run directories with secondary data for Table I of the paper "Big bang nucleosynthesis limits and relic gravitational waves detection prospects" by T. Kahniashvili, E. Clarke, J. Stepp, & Axel Brandenburg. If anything turns out to be incomplete, please email brandenb@nordita.org. </pre>
Dataset from: Gravitational wave sources in our Galactic backyard - Predictions for BHBH, BHNS and NSNS binaries in LISA
<p>The data from all simulations used in "<em><strong>Gravitational wave sources in our Galactic backyard: Predictions for BHBH, BHNS and NSNS binaries in LISA</strong></em>"</p> <p>Contents:</p> <ul> <li><strong>detections_and_totals.zip</strong> <ul> <li>Contains for .npy files that contain tables of the detections and total DCOs in Milky Way. These were calculated with <a href="https://github.com/TomWagg/detecting-DCOs-in-LISA/blob/main/simulation/postprocessing_notebooks/get_detection_rates.ipynb">this</a> and <a href="https://github.com/TomWagg/detecting-DCOs-in-LISA/blob/main/simulation/postprocessing_notebooks/get_total_DCOs_in_MW.ipynb">this</a> notebook and are included for convenience so you don't have to re-run these notebooks</li> </ul> </li> <li><strong>simulations_4yr.zip</strong> <ul> <li>Contains 60 .h5 files that contain the main simulations for a 4-year LISA mission. Each file contains the results for a single DCO type (BHBH, BHNS or NSNS) and model variation (20 variations) are labeled as <em>{DCO_type}_{variation}_all.h5</em><strong><em>. </em></strong></li> </ul> </li> <li><strong>simulations_10yr.zip</strong> <ul> <li>As simulations_4yr.zip but for a 10-year LISA mission</li> </ul> </li> <li><strong>simple_mw_simulations.zip</strong> <ul> <li>As simulations_4yr.zip but using a simple model for the Milky Way (discussed in Appendix D) and only for models A and F (hence only contains 6 .h5 files)</li> </ul> </li> </ul> <p>For a description of how to use these files to reproduce figures and results see the README.md in the associated GitHub repository: <a href="https://github.com/TomWagg/detecting-DCOs-in-LISA">https://github.com/TomWagg/detecting-DCOs-in-LISA</a></p> <p>Version 0.0.1 - Changes model E to E' as discussed in paper (now we allow HeHG donors to survive common-envelopes)</p> <ul> </ul>
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.