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142 results for “gravitational waves”
Submitted Completed pointings to the Gravitational Wave Treasure Map for event MS181101ab
Attached in a .json file is the completed pointing information for 25 observation(s) for the EM counterpart search associated with the gravitational wave event MS181101ab. These observations were taken on the DECam, ZTF, and DLT40 instruments.
Costless correction of chain based nested sampling parameter estimaion in gravitational wave data and beyond (supplementary material)
<p>These are the nested sampling inference products used to obtain the results for <span><a href="https://arxiv.org/abs/2404.16428">arXiv:2404.16428</a>. The chains are given as pickled dataframes as there are over 1000 runs provided here. The script for reproducing the plots in the paper is also given. </span></p> <p><span>Folders:</span></p> <p><span>outdir_simulated_BBH - contains 200 runs on the same simulated signal. 'samples' contains the full sets of weighted samples from each run and 'full_dfs' contains the dataframes with the weighted samples AND the phantom points from the run. The samples in 'full_dfs' with chain_no=0 are the weighted samples, and all others are phantoms. </span></p> <p><span>outdir_test - contains the single run on the above simulated signal which was performed with num_repeats=25*ndims=100. </span></p> <p><span>outdir_coverage - contains 1000 runs on different simulated signals, with parameters drawn from the prior. The drawn values are saved as '{}_params.npy' for each run.</span></p>
Simulations from: Formation of the first three gravitational-wave observations through isolated binary evolution
<p>The results of all simulations shown and discussed in "<a href="https://www.nature.com/articles/ncomms14906">Formation of the first three gravitational-wave observations through isolated binary evolution</a>", published in Nature Communications, 8, 14906 (<a href="https://arxiv.org/abs/1704.01352">arXiv</a>).</p> <p>Contents:</p> <p>README<br> Z0_002_LVT151012_1475624202.dat<br> Z0_001_GW150914_1394278271.dat<br> Z0_002_allMergers.dat<br> Z0_001_GW151226_294773844.dat<br> Z0_002_alphalambda0_01_allMergers.dat<br> Z0_001_LVT151012_784739229.dat<br> Z0_002_fLBV_10_allMergers.dat<br> Z0_001_allMergers.dat<br> Z0_005_GW151226_1432529107.dat<br> Z0_001_alphalambda0_01_allMergers.dat<br> Z0_005_LVT151012_1051180640.dat<br> Z0_001_fLBV_10_allMergers.dat<br> Z0_005_allMergers.dat<br> Z0_002_GW151226_997696601.dat</p> <p>All simulations made using <a href="http://compas.science/">COMPAS</a></p>
Inference results from "No need to know: astrophysics-free gravitational-wave cosmology"
<p>Simulated GW data and inference results for all runs associated with the publication "No need to know: astrophysics-free gravitational-wave cosmology." This accompanies the code used to make the paper and run all analyses, hosted at: https://github.com/afarah18/spectral-sirens-with-GPs</p> <p> </p> <p>v4 and v5: updated after peer review changes</p>
Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification
<p>This dataset contains gravitational wave (GW) data used in our research work <a href="https://doi.org/10.1088/2632-2153/ada33a" target="_blank" rel="noopener">Abylkairov et al. (2024)</a>. The first 10,000 columns represent the gravitational wave strain <em>D · h</em> [cm] for the corresponding time values ranging from -993 ms to 6.9 ms, with a step size of 0.1 ms. The zero time refers to the time of core bounce. Each row within these first 10,000 columns corresponds to 864 different gravitational wave signals.</p> <p>Columns 10,001 to 10,005 contain the following additional parameters:</p> <ul> <li><strong>T/|W|</strong>: The rotational parameter.</li> <li><strong>GR_or_GREP</strong>: Binary indicator for the signal type, where 0 denotes GR and 1 denotes GREP.</li> <li><strong>EOS</strong>: The equation of state (EOS) model, where 0 corresponds to SFHo, 1 to LS220, 2 to HSDD2, and 3 to GShenFSU2.1.</li> <li><strong>f_peak</strong>: The peak frequency [Hz].</li> <li><strong>D Delta h</strong>: <em>D · ∆h</em> [cm].</li> </ul> <p>For each row (representing a single gravitational wave signal), these parameters provide information about the signal's rotational parameter, type (GR or GREP), EOS model, peak frequency, and <em>D · ∆h</em>.</p> <p><strong>Note</strong>: In the f_peak calculation procedure, we truncated the GW signal at 4.5 ms after the end of the core bounce (see <a href="https://doi.org/10.1103/PhysRevD.95.063019">Richers et al. (2017)</a> for details).</p>
Dataset accompaning "Variational inference for correlated gravitational wave detector network noise"
<p>Dataset accompaning the paper "<strong>Variational inference for correlated gravitational wave detector network noise</strong>"</p> <p> </p> <h3>Raw data files</h3> <ul> <li>ET_caseA_noise.h5 (correlated noise)</li> <li>ET_caseB_noise.h5 (uncorrelated noise)</li> </ul> <p>These contain:</p> <ul> <li>raw_XYZ (the XYZ channels of ET noise)</li> <li>time (time in seconds, corresponding to raw_XYZ)</li> <li>periodogram: <ul> <li>pdgrm (of the above channels, truncated to 5-128 Hz)</li> <li>freq (in Hz)</li> </ul> </li> <li>true_psd <ul> <li>psd </li> <li>freq</li> </ul> </li> </ul> <p><em>Note</em>: case C from the manuscript utilised the case B dataset, (but with a model that does not account for the cross-spectrum). It does not have a separate dataset. </p> <p> </p> <h3><strong>Result file</strong></h3> <ul> <li>ET-CaseA-SGVB-PSD.h5</li> <li>ET-CaseB-SGVB-PSD.h5</li> <li>ET-CaseC-SGVB-PSD.h5</li> </ul> <p>These contain:</p> <ul> <li>psd_quantiles (the lower 0.05, median 0.50, upper 0.95 quantiles of 500 PSD samples)</li> <li>freq (in Hz, associated with the psd_quantiles)</li> </ul>
Gravitational Wave Memory Imprints on the CMB from Populations of Massive Black Hole Mergers
<p>Visualisation videos of the effect of gravitational wave (GW) memory onto photons from the cosmic microwave background (CMB).</p>
Datasets for "Polarization of gravitational waves from helical MHD turbulent sources"
<p>The tar archive helical.tar contains the run directories for each run<br> in Table 1 and the figures of the paper "Polarization of gravitational<br> waves from helical MHD turbulent sources", https://arxiv.org/abs/2107.05356.<br> by A. Roper Pol, S. Mandal, A. Brandenburg, and T. Kahniashvili.<br> The run directories, the plots, and the code to generate the plots can be<br> found in https://github.com/AlbertoRoper/GW_turbulence.</p>
Gravitational waves from freely decaying turbulence: simulation visualisations
<p>Simulation visualisations to accompany the paper <em>Generation of gravitational waves from freely decaying turbulence</em>, <a href="https://arxiv.org/abs/2205.02588">arXiv:2205.02588</a>, which has been <a href="https://doi.org/10.1088/1475-7516/2022/09/029">published in JCAP</a>. The visualisations are provided in two formats.</p> <p>The visualisations correspond to simulations A' and D, as listed in Table 2 of the paper. The magnitude of the fluid 3-velocity is shown.</p>
Animations and figures for Report - If light is stretched by gravitational waves, why can we use light as a ruler to detect them?
<p>Package for .gifs and images for report, the two .gif animations are to better represent the images used to explain the entire process from section 3.</p>
Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event TEST_EVENT Preliminary
Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event TEST_EVENT Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2020arXivNicePaper
Submitted Completed pointings to the Gravitational Wave Treasure Map for event TEST_EVENT
Attached in a .json file is the completed pointing information for 1 observation(s) for the EM counterpart search associated with the gravitational wave event TEST_EVENT. These observations were taken on the DLT40 instrument.
Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS230322s Preliminary
Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS230322s Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2020arXivNicePaper
Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS230322s Preliminary
Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS230322s Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2020arXivNicePaper
Submitted Completed pointings to the Gravitational Wave Treasure Map for event TEST_EVENT
Attached in a .json file is the completed pointing information for 14 observation(s) for the EM counterpart search associated with the gravitational wave event TEST_EVENT. These observations were taken on the DLT40 instrument.
Constraining gravitational wave amplitude birefringence with GWTC-3: Data Release
<p>Dataset release accompanying Constraining gravitational wave amplitude birefringence with GWTC-3.</p>
The second data release from the European Pulsar Timing Array II. Customised pulsar noise models for spatially correlated gravitational waves
<p>Aims: The nanohertz gravitational wave background (GWB) is expected to be an aggregate signal of an ensemble of gravitational waves emitted predominantly by a large population of coalescing supermassive black hole binaries in the centres of merging galaxies. Pulsar tiNanohertz ming arrays (PTAs), which are ensembles of extremely stable pulsars at approximately kiloparsec distances precisely monitored for decades, are the most precise experiments capable of detecting this background. However, the subtle imprints that the GWB induces on pulsar timing data are obscured by many sources of noise that occur on various timescales. These must be carefully modelled and mitigated to increase the sensitivity to the background signal. Methods: In this paper, we present a novel technique to estimate the optimal number of frequency coefficients for modelling achromatic and chromatic noise, while selecting the preferred set of noise models to use for each pulsar. We also incorporated a new model to fit for scattering variations in the Bayesian pulsar timing package temponest. These customised noise models enable a more robust characterisation of single-pulsar noise. We developed a software package based on tempo2 to create realistic simulations of European Pulsar Timing Array (EPTA) datasets that allowed us to test the efficacy of our noise modelling algorithms. Results: Using these techniques, we present an in-depth analysis of the noise properties of 25 millisecond pulsars (MSPs) that form the second data release (DR2) of the EPTA and investigate the effect of incorporating low-frequency data from the Indian Pulsar Timing Array collaboration for a common sample of ten MSPs. We used two packages, enterprise and temponest, to estimate our noise models and compare them with those reported using EPTA DR1. We find that, while in some pulsars we can successfully disentangle chromatic from achromatic noise owing to the wider frequency coverage in DR2, in others the noise models evolve in a much more complicated way. We also find evidence of long-term scattering variations in PSR J1600-3053. Through our simulations, we identify intrinsic biases in our current noise analysis techniques and discuss their effect on GWB searches. The analysis and results discussed in this article directly help to improve the sensitivity to the GWB signal and they are already being used as part of global PTA efforts.</p>
How an interferometric gravitational wave detector works (animation)
<p>Animation of a simplified model for an interferometric gravitational wave detector. A description as well as additional background information are given in section 7.4 of the review https://arxiv.org/abs/1812.11589 of using models for teaching about general relativity.</p>
Parameter estimation data release for paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method"
<p>This is a second data release for the paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method", which is available on <a href="https://arxiv.org/abs/2306.12908">https://arxiv.org/abs/2306.12908</a>.</p> <p>This data release contains posterior samples and configuration files for parameter estimation runs performed for the paper. For the lensing hypothesis tests results see the other <a href="https://doi.org/10.5281/zenodo.8409635">data release</a> for the same paper.</p> <p>For each event that we have run parameter estimation for, we provide a tar.gz file that includes the configurations, the priors and the results for each run, typically with several different waveforms. All runs were performed with <a href="https://lscsoft.docs.ligo.org/parallel_bilby/">parallel bilby</a> with different versions as described in section 6.1 of the paper. The input data is available from <a href="https://gwosc.org/">GWOSC</a>.</p> <p>The results for IMRPhenom* waveforms (runs with parallel bilby version 1.0.1) are in json format, while for SEOBNRv5PHM and NRSur7dq4 (run with parallel bilby version 2.0.2) some of the results are in hdf5 and others in json, depending on whether multiple runs were merged together. These should be readable with bilby or <a href="https://lscsoft.docs.ligo.org/pesummary/">pesummary</a>.</p> <p>We provide the "complete" configuration files processed by bilby. These can be used for reproducing the runs, but the prior file information needs to be added with a syntax like the following `prior-file=ProdF4.prior`. Generally most runs use the same prior file, with the labels `Prod*.prior`, taken from the LVK analyses. The priors used for the aligned-spin waveforms have the label `_AS`. For the NRSur7dq4 waveform we use a restricted prior with the label `_NR_Sur_constrMtot`. For GW190527 we also use a different prior for IMRPhenomXP and IMRPhenomTPHM, which has the label `_restricted`.</p> <p>Also note that for the result file GW190527_NRSur7dq4_N4096_nact50_fmin0_nparallel3_merged_result.hdf5 this was merged from the results obtained with the config GW190527_NRSur7dq4_N4096_nact50_fmin0_nparallel3_config_complete.ini together with the fourth chain of identical configuration.</p>
Eclipses of continuous gravitational waves as a probe of stellar structure
<p>Jupyter notebooks to reproduce the results and figures of the paper "Eclipses of continuous gravitational waves as a probe of stellar structure". Input files and data produced from the MESA simulations in the paper is also included. MESA version used is 10398.</p> <p>The jupyter notebooks included are:</p> <p>- Notebook.ipynb: Containing all calculations and plots except for the pop-synth section.</p> <p>- accreting_NS.ipynb: Containing the calculations and plots for the pop-synth section.</p> <p>Further details can be found inside these notebooks.</p> <p> </p> <p>The mesa input files are contained in</p> <p>- mesa_models/sun: For the solar model used.</p> <p>- mesa_models/LMXB: For the low mass X-ray binary model used.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.