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The population of merging compact binaries inferred using gravitational waves through GWTC-3 - Data release
<p>Data associated with Figures, Tables, and population parameter samples associated with <br><strong>The population of merging compact binaries inferred using gravitational waves through GWTC-3 , </strong><br><strong><a href="https://dcc.ligo.org/LIGO-P2100239/public">LIGO DCC</a>, <a href="https://arxiv.org/abs/2111.03634">arXiv</a>, <a href="https://journals.aps.org/prx/abstract/10.1103/PhysRevX.13.011048">PRX</a>. </strong><br>This is v3, superseding v2. Please see the README.md for more information.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Data Quality Products for GW Searches
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p>This release contains two data-quality products that are used by search analyses to help mitigate non-Gaussian noise in the detector data. Gating removes short-duration artifacts from the data by smoothly rolling the affected data to zero. The <a href="https://doi.org/10.1088/2632-2153/abab5f">iDQ glitch likelihood</a> uses machine learning to predict the probability that a non-Gaussian transient is present using information from auxiliary channels.</p> <p><br> <strong>Gating files used in analyses of O3 LIGO data</strong></p> <p>As a pre-processing step, the <a href="https://pycbc.org/">PyCBC</a> search pipeline uses an inverted-Tukey window to mitigate the effect of loud, non-Gaussian features in the data. This is further described in <a href="https://dx.doi.org/10.1088/0264-9381/33/21/215004">Usman <em>et al.</em> 2016</a>.</p> <p>A subset of these times are the times listed in the txt files</p> <ul> <li>H1-O3_GATES_1238166018-31197600.txt</li> <li>L1-O3_GATES_1238166018-31197600.txt</li> </ul> <p>These times in these files were chosen based on auxiliary monitors of overflows in the digital-to-analog converters used to control the positions of the test masses. The gated times (i.e. the time period where the data is zeroed) are time segments where these monitors recorded an overflow were. The central time and suggested half-width of zero time were chosen to fully cover these time seconds. The final gating parameter, the suggested taper time was chosen to be 0.5 to balance the cost of impacting more data with the window function versus introducing additional artifacts into the data.</p> <p>The syntax of the files themselves is</p> <p>{central time} {suggested half-width of zero time} {suggested taper time}</p> <p>with each row containing the parameters of a single gate.</p> <p>The included notebook provides an example of how to read in and apply one of the suggested gates.</p> <p><br> <strong>Renormalized iDQ timeseries</strong></p> <p>The renormalized iDQ timeseries data-quality product is used within the GstLAL search pipeline to generate results for GWTC-3. This data product was found to be statistically helpful in improving data quality within the <a href="https://lscsoft.docs.ligo.org/gstlal/">GstLAL</a> search pipeline. This is further described in <a href="https://arxiv.org/abs/2010.15282">Godwin <em>et al</em>. 2020</a>.</p> <p>This file contains a time series for each LIGO detector related to the probability of a glitch in the strain data given the behavior in analyzed auxiliary channels monitoring the behavior of the detectors and their environment.</p> <ul> <li>H1L1-IDQ_TIMESERIES-1256655642-12905976.h5</li> </ul> <p>The HDF5-formatted file contains two groups, H1 and L1, corresponding to LIGO Hanford and LIGO Livingston, respectively. Each group contains several datasets; the data dataset corresponds to the renormalized iDQ log-likelihoods, as described in <a href="http://doi.org/10.1088/2632-2153/abab5f">Godwin <em>et al</em>. 2020</a>, and the time dataset corresponds to the times associated with the renormalized iDQ log-likelihoods in the data dataset.</p> <p> </p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5636795 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>. </p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Data Quality Products for GW Searches
<p>This material is part of several data products associated with GWTC-2.1, the deep extended catalog of compact binary coalescences observed by the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration and the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration during the first half of the third observing run. For further information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2100063/public">dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">www.gw-openscience.org/GWTC-2.1/</a>).</p> <p>This release contains data quality products that are used by search analyses to help mitigate non-Gaussian noise in the detector data.</p> <p><strong>Renormalized iDQ timeseries</strong></p> <p>This release contains the renormalized iDQ timeseries data quality product used within the GstLAL search to generate results for GWTC-2.1 as described in <a href="https://arxiv.org/abs/2010.15282">Goodwin <em>et al</em>. 2020</a>. This data product was found to be statistically helpful in improving data quality within the GstLAL search. For further information about iDQ see <a href="https://iopscience.iop.org/article/10.1088/2632-2153/abab5f">Essick <em>et al</em>. 2020</a>.</p> <p>The file </p> <ul> <li>H1L1-IDQ_TIMESERIES-1238166018-15843600.h5</li> </ul> <p>contains a time series for each LIGO detector related to the probability of a glitch in the strain data given the behavior in the analyzed auxiliary channels which monitor the behavior of the detectors and their environment.</p> <p>The HDF5-formatted file contains two groups, H1 and L1, corresponding to LIGO Hanford and LIGO Livingston, respectively. Each group contains several datasets; the data dataset corresponds to the renormalized iDQ log-likelihoods, as described in Godwin <em>et al</em>. 2020, and the time dataset corresponds to the times associated with the renormalized iDQ log-likelihoods in the data dataset.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the guide to <a href="https://doi.org/10.1088/1361-6382/ab685e">LIGO-Virgo data analysis</a>.</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Parameter Estimation Data Release
<p>This material is part of several data products associated with GWTC-2.1, an update to the second Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2100063/public">https://dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">https://www.gw-openscience.org/GWTC-2.1/</a>).</p> <p><strong>Parameter estimation data release</strong></p> <p>This data release contains posterior samples (*.h5) for gravitational-wave candidates through the first part of the third observing run (O3a). We provide results for the 44 candidates that have a probability of astrophysical origin of over 0.5 from O3 as well as the 10 previously-reported binary-black-hole candidates from GWTC-1 (this excludes GW170817). There are two .h5 files per event</p> <ul> <li> <p>Cosmologically reweighted (*cosmo.h5)</p> </li> <li> <p>Not cosmologically reweighted (*nocosmo.h5)</p> </li> </ul> <p>The cosmologically reweighted posteriors are reweighted to have a luminosity-distance prior that has a uniform merger rate in the source's comoving frame. Each .h5 file contains samples for multiple runs with keys C01:RUN_NAME, where RUN_NAME is the waveform used for the run (and additional prior-choice information if necessary) or Mixed, indicating an equal mixture of samples from runs with similar physics if they exist. In cases where only one waveform was used, the Mixed dataset is simply a resampling of those results . GW190425 does not have Mixed samples. See the <a href="https://dcc.ligo.org/LIGO-P2100063/public">paper</a> appendices for further information. In addition to containing the posterior samples, the .h5 files also contain metadata about the analyses including the configuration files (which specify details such as the detector data analyzed), noise power spectral densities (potentially for a superset of the detectors used in the analysis) and calibration uncertainty envelopes.</p> <p>The python notebook explains how to use the posterior samples. This data release also contains .FITS skymap files, which can be read with <a href="https://lscsoft.docs.ligo.org/ligo.skymap/#">ligo.skymap</a>, and skymap statistics in *.txt files.</p> <p>The inference of the source parameters were performed with <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby</a>, <a href="https://lscsoft.docs.ligo.org/parallel_bilby/">Parallel Bilby</a> and <a href="https://git.ligo.org/richard-oshaughnessy/research-projects-RIT/tree/temp-RIT-Tides">RIFT</a>. The results are formatted using <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a>.</p> <p><a href="https://zenodo.org/record/5546663#.YnAAcvPMKqC">A similar release has been made to accompany GWTC-3</a> for results from the second part of the third observing run.</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5117702 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p>For more general background on gravitational-wave parameter estimation, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
GWTC-2.1: Deep extended-catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Sensitivity of search pipelines to simulated signals
<p>Results of search pipelines (GSTLAL, MBTA, PYCBC, PYCBC BBH) used to identify candidates in <a href="https://dcc.ligo.org/LIGO-P2100063/public">GWTC-2.1</a> on a set of simulated signals corresponding to binary neutron star (BNS), neutron star black holes (NSBH), and binary black holes (BBH) signals. Additionally, we include a README file which provides information on how to read these files.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — O3 search sensitivity estimates
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the papers (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a> and <a href="http://dcc.ligo.org/LIGO-P2100239/public">dcc.ligo.org/LIGO-P2100239/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>Observing Run 3 (O3) Search Sensitivity Estimates</strong></p> <p>This document contains HDF injection summary files for search sensitivity estimates spanning the LIGO–Virgo–KAGRA (LVK) Collaborations' third observing run (O3).</p> <p>Details of the individual files can be found in</p> <ul> <li> o3-sensitivity-estimates.md</li> </ul> <p>including descriptions of the injected distributions and the HDF file format adopted.</p> <p>Separate files are provided for the two parts of the run, O3a and O3b, specified by the GPS start times and durations in the filenames, and for the entire O3 run (filename with no times specified).</p> <p>Separate files are also provided for subpopulations that span the Binary Neutron Star (bns), Neutron Star–Black Hole (nsbh), Binary Black Hole (bbh), and Intermediate Mass Black Hole (imbh) mass ranges. The subpopulations are combined into a single file (mixture) containing a mixture model that spans the union of all subpopulation.</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5546675 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave search analyses, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p> <p> </p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — O1+O2+O3 Search Sensitivity Estimates
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the papers (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a> and <a href="http://dcc.ligo.org/LIGO-P2100239/public">dcc.ligo.org/LIGO-P2100239/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>O1 + O2 + O3 Search Sensitivity Estimates</strong></p> <p>This document contains HDF injection summary files for search sensitivity estimates spanning the LIGO–Virgo–KAGRA (LVK) Collaborations' first (O1), second (O2), and third (O3) observing runs.</p> <p>Details of individual files can be found in</p> <ul> <li> o1+o2+o3-sensitivity-estimates.md</li> </ul> <p>including descriptions of the HDF file format adopted.</p> <p>Separate files are provided for individual subpopulations that span the Binary Neutron Star (bns), Neutron Star–Black Hole (nsbh), Binary Black Hole (bbh), and Intermediate Mass Black Hole (imbh) mass ranges. Additionally, a single file spanning the union of those mass ranges (mixture) is provided.</p> <p>Sensitivity estimates for O1 and O2 are available via semi-analytic methods (estimates of the optimal network signal-to-noise ratio). Sensitivity estimates for O3 are available from real search results. Analysts should specify detection thresholds separately for each type of sensitivity estimate (e.g., a signal-to-noise cut for O1+O2 and a false alarm rate cut for O3).</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5636815 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave search analyses, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Parameter estimation data release
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>Parameter estimation data release</strong></p> <p>This data release contains posterior samples (*.h5) for gravitational-wave candidates from the second part of the third observing run (O3b).We provide results for the 35 candidates that have a probability of astrophysical origin of over 0.5, plus <a href="https://doi.org/10.3847/2041-8213/ac082e">GW200105_162426</a>, which is a clear outlier from the noise background. There are two .h5 files per event</p> <ul> <li>Cosmologically reweighted (*cosmo.h5)</li> <li>Not cosmologically reweighted (*nocosmo.h5)</li> </ul> <p>The cosmologically reweighted posteriors are reweighted to have a luminosity-distance prior that has a uniform merger rate in the source's comoving frame. See the <a href="http://dcc.ligo.org/LIGO-P2000318/public">paper</a> appendices for further information. In addition to containing the posterior samples, the .h5 files also contain metadata about the analyses including the configuration files (which specify details such as the detector data analysed), noise power spectral densities (potentially for a superset of the detectors used in the analysis) and calibration uncertainty envelopes.</p> <p>The inference of the source parameters were performed with <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby</a>, <a href="https://lscsoft.docs.ligo.org/parallel_bilby/">Parallel Bilby</a> and <a href="https://git.ligo.org/richard-oshaughnessy/research-projects-RIT/tree/temp-RIT-Tides">RIFT</a>. The results are formatted using <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a>.</p> <p><strong>A note about mixed samples:</strong> The samples provided here are produced using different waveform approximants. The Mixed label indicates that equal numbers of samples have been included from two different waveform approximants. For the binary black holes, these are IMRPhenomXPHM and SEOBNRv4PHM (for more details, see GWTC3p0PEDataReleaseExample.ipynb included in this data release and the paper). As different waveforms were analysed with different codes, there are sometimes differences in some parameters due to conventions in the codes. For example:</p> <ul> <li>As RIFT does not sample over time of coalescence as Bilby does, the RIFT time of coalescence results have a posterior distribution with a single spike, whereas the Bilby results have a distribution of peaks representing different sky positions for the source.</li> <li>There are different conventions for the range of the polarization angle (either 0 to π or 0 to 2 π). The parameter psi_wrapped maps all results to the range 0 to π, should consistency be important.</li> <li>The likelihood may show small differences when different sampling rates were used for Bilby and RIFT. The log-likelihood is expected to have a relative shift between the two runs of a few nats.</li> </ul> <p>Due to these differences, care must be taken when using Mixed samples, which will contain results using both codes' conventions. This should not impact the most interesting quantities, such as the masses, and so should only be rarely an issue.</p> <p>A <a href="https://doi.org/10.5281/zenodo.5117702">similar parameter-estimation release has been made to accompany GWTC-2.1</a> for results from the first part of the third observing run.</p> <p><strong>Sky localization data release</strong></p> <p>The sky localization tar file (IGWN-GWTC3p0-v2-PESkyLocalizations.tar.gz) contains candidate sky localizations corresponding to different parameter estimation configurations (.fits). Two waveforms are used for the majority of targets (IMRPhenomXPHM and SEOBNRv4PHM) and additional waveforms are used for possible neutron star--black hole mergers (see the <a href="https://dcc.ligo.org/LIGO-P2000318/public">paper</a> for further information). If you do not mind which waveform, the sky localizations labelled "Mixed" include posterior samples from both waveforms used. A machine readable list (skyLocalizationFileList.csv) of sky localization files is included within the .tar.gz file for ease of use, where the Mixed results are indicated as Default=True.</p> <p><strong>Contour data release</strong></p> <p>The contour tar file (IGWN-GWTC3p0-v2-PEContours.tar.gz) contains the contour files used to produce Figures 8 and 9 in the <a href="http://dcc.ligo.org/LIGO-P2000318/public">paper</a>. The python notebook (GWTC3p0PEPlotContourData.ipynb) explains how to reproduce these figures (and an interactive version of these plots can be accessed at <a href="https://gwtc3-contours.streamlit.app/">gwtc3-contours.streamlit.app/</a>).</p> <p><strong>Python notebook</strong></p> <p>The Python notebook (GWTC3p0PEDataReleaseExample.ipynb) explains how to read and use the posterior samples with a selection of examples.</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5546662 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave parameter estimation, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Candidate data release
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>Candidate data release</strong></p> <p>Data associated with candidates in GWTC-3. These are gravitational-wave candidates from the the third observing run (O3) of the Advanced LIGO and Advanced Virgo detectors that pass a false alarm rate threshold of 2/day. We upload a tar file (search_data.tar.gz) containing all the data and a python notebook (GWTC3_search_data.ipynb) that provides a description on how to use the files contained in the dataset.</p> <p>Associated with each candidate are the search analysis results and a localization (assuming that the source is astrophysical). Four search analysis pipelines have been used: the templated-based <a href="https://lscsoft.docs.ligo.org/gstlal/">GstLAL</a>, <a href="https://doi.org/10.1088/1361-6382/abe913">MBTA</a> and <a href="https://pycbc.org/">PyCBC</a>, plus the template-free <a href="https://gwburst.gitlab.io/">cWB</a>. Localizations from the template-based pipelines are calculated using <a href="https://lscsoft.docs.ligo.org/ligo.skymap/bayestar/index.html">Bayestar</a>, while cWB candidates are calculated by cWB itself.</p> <p>This release is primarily composed of results from the second part of O3 (O3b), but also includes a subset of results from the first part (O3a). A similar release was made for the previous <a href="https://doi.org/10.5281/zenodo.5117761">GWTC-2.1</a> that contained candidates from the first part of O3 (O3a) from GstLAL, MBTA and PyCBC. We include updated probabilities of astrophysical origin for these candidates: each search analysis has a o3a_pastro directory that contains these results. Since GWTC-2.1 did not include cWB results, this release includes cWB O3a candidates in addition to O3b: the cWB directory contains a subdirectory called o3a_events that contains the O3a results.</p> <p>The probability of astrophysical origin is calculated assuming a compact binary coalescence source, which may not always be appropriate for the template-free cWB analysis.</p> <p> </p> <p>For more general background on gravitational-wave search analysis and sky maps, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Glitch modelling for events
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>Glitch model for GWTC-3 events</strong></p> <p>Glitch model for events in the GWTC-3 catalog that used either <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> glitch subtraction or linear noise subtraction.</p> <p>Each data file for events processed with BayesWave contain three channels:</p> <ol> <li>The calibrated strain data, including any glitches that are present,</li> <li>A model of the glitches, produced using the BayesWave algorithm,</li> <li>The calibrated data with the glitch model subtracted, used for parameter estimation.</li> </ol> <p>Each data file for events processed with linear noise subtraction contain one channel:</p> <ol> <li>The calibrated data with the glitch linearly subtracted, used for parameter estimation.</li> </ol> <p>LIGO Hanford data for events GW191109_010717, GW191113_071753, GW191127_050227, and GW191219_163120 was generated with BayesWave. The names and sample rates (in Hz) of the channels in these files are</p> <ol> <li>H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 16384</li> <li>H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_glitch 16384</li> <li>H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_T1700406_v4 16384</li> </ol> <p>LIGO Livingston data for events GW191109_010717, GW191219_16312, GW200105_162426, and GW200115_042309 was generated with BayesWave. The names and sample rates (in Hz) of the channels in these files are</p> <ol> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 16384</li> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_glitch 16384</li> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_T1700406_v4 16384</li> </ol> <p>Virgo data for event GW191105_143521 was generated with BayesWave. The names and sample rates (in Hz) of the channels in this file are</p> <ol> <li>V1:Hrec_hoft_16384Hz 16384</li> <li>V1:Hrec_hoft_16384Hz_glitch 16384</li> <li>V1:Hrec_hoft_16384Hz_T1700406_v4 16384</li> </ol> <p>LIGO Livingston data for event GW200129_065458 was generated with linear noise subtraction. The name and sample rate (in Hz) of the channel in this file is</p> <ol> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_P1800169_v4 16384</li> </ol> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5546679 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Candidate Data Release
<p>Data associated with candidates in <a href="https://dcc.ligo.org/LIGO-P2100063/public">GWTC-2.1</a>. These are gravitational-wave candidates from the first half of the third observing run (O3a) of the Advanced LIGO and Virgo detectors that pass a false alarm rate threshold of 2/day. We upload a tar file (search_data_GWTC2p1.tar.gz) containing all the data and a python notebook (search_data.ipynb) which provides description on how to use the files contained in the dataset.</p>
BHBH simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers
<p>The data for all <strong>BHBH </strong>simulations shown in<em><strong> "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers". </strong>Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)</em></p> <p> </p> <p><strong>Contents: </strong></p> <ul> <li><strong>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E) and "unstable case BB + optimistic CE" (model F) files.</strong><br> <strong>These zip files are: </strong> <ul> <li><em>fiducial.zip, </em> the Fiducial model (A) and Optimistic CE model (K)</li> <li><em>massTransferEfficiencyFixed_0_25.zip, </em>the <span class="math-tex">\(\beta\)</span> = 0.25 model (B) </li> <li><em>massTransferEfficiencyFixed_0_5.zip</em>, the <span class="math-tex">\(\beta\)</span> = 0.5 model (C) </li> <li><em>massTransferEfficiencyFixed_0_75.zip,</em> the <span class="math-tex">\(\beta\)</span> = 0.75 model (D)</li> <li><em>unstableCaseBB.zip, </em>the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li><em>alpha0_1 zip</em>, the <span class="math-tex">\(\alpha = 0.1\)</span> model (G) </li> <li><em>alpha0_5.zip</em>, the <span class="math-tex">\(\alpha = 0.5\)</span> model (H) </li> <li><em>alpha2_0.zip</em>, the <span class="math-tex">\(\alpha = 2.0\)</span> model (I) </li> <li><em>alpha10_0.zip</em>, the <span class="math-tex">\(\alpha = 10.0\)</span> model (J) </li> <li><em>rapid.zip</em>, the rapid SN model (L) </li> <li><em>maxNSmass2_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\)</span> model (M) </li> <li><em>maxNSmass3_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\)</span> model (N)</li> <li><em>noPISN.zip</em>, the no PISN model (O) </li> <li><em>ccSNkick_100km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 100 km/s model (P) </li> <li><em>ccSNkick_30km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 30 km/s model (Q)</li> <li> <em>noBHkick.zip, </em>the no BH SN kick model (R)</li> <li><em>wolf_rayet_multiplier_0_1.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 0.1\)</span> (S)</li> <li><em>wolf_rayet_multiplier_5.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 5\)</span> (T)<br> <br> </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates: <ul> <li><strong>csvFilesForFigure1_DCOpaper.zip </strong># contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li><strong>csvFilesForFigure2_and_3_DCOpaper.zip </strong># contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names: <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Details of how to use the data (a readme), as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a> </p> <p>If you use this data, please cite </p> <p>Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a></p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Glitch modelling for events
<p>This material is part of several data products associated with GWTC-2.1, the deep extended catalog of compact binary coalescences observed by the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration and the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration during the first half of the third observing run. For further information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2100063/public">dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">www.gw-openscience.org/GWTC-2.1/</a>).</p> <p><strong>Glitch model for GWTC-2.1 events</strong></p> <p>Glitch model for events in the GWTC_2.1 catalog that used <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> glitch subtraction. This includes LIGO Livingston Observatory (L1) data for the following events:</p> <ul> <li>GW190413_134308</li> <li>GW190425_081805</li> <li>GW190503_185404</li> <li>GW190513_205428</li> <li>GW190514_065416</li> <li>GW190701_203306</li> <li>GW190924_021846</li> </ul> <p>Each data file contains three channels:</p> <ol> <li>the calibrated strain data, including any glitches that are present,</li> <li>a model of the glitches, produced using the BayesWave algorithm,</li> <li>the calibrated data with the glitch model subtracted, used for parameter estimation</li> </ol> <p>For the L1 data for all events, these channels have the following names and sample rates (in Hz):</p> <ol> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 16384</li> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_glitch 16384</li> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_T1700406_v4 16384</li> </ol> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI</code></pre> <p>where the record ID for the most recent version of this page is 6477075 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the guide to <a href="https://doi.org/10.1088/1361-6382/ab685e">LIGO-Virgo data analysis</a>.</p>
Main Sequence + Compact Object binary candidates from Gaia DR3 astrometric and spectroscopic excess noise
<p>MS+CO systems selected from Gaia DR3 via inferred periods and mass ratios derived from astrometric and spectroscopic errors.</p> <p>The sample is split into a bronze list (significant astrometric and spectroscopic RUWE, mass ratio > 1 and companion mass > 3 Msun). </p> <p>A subset of these is chosen as a silver list (propagating errors on mass ratio and companion mass to deselect systems which are not significantly above the previous criteria)</p> <p>Finally, a gold list is constructed from the subset of the silver list which shows no evidence of being significantly brighter than a single MS star and with no significant excess photometric noise.</p> <p>We include the most relevant Gaia data for the system, as well as our inferred spectroscopic and photometric errors and RUWEs, and the inferred periods and mass ratios. Gaia's DR3 source id, and the ra, dec position are included and thus other Gaia data, or data from other astronomical catalogs, can be found for these systems.</p> <p>The catalog and the underlying methods are explained in more detail in <a href="https://arxiv.org/abs/2206.04392">Andrew et al. 2022</a>.</p>
NSNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers
<p>The data for all <strong>NSNS </strong>simulations shown in<em><strong> "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers". </strong>Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)</em></p> <p> </p> <p><strong>Contents: </strong></p> <ul> <li><strong>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E) and "unstable case BB + optimistic CE" (model F) files.</strong><br> <strong>These zip files are: </strong> <ul> <li><em>fiducial.zip, </em> the Fiducial model (A) and Optimistic CE model (K)</li> <li><em>massTransferEfficiencyFixed_0_25.zip, </em>the <span class="math-tex">\(\beta\)</span> = 0.25 model (B) </li> <li><em>massTransferEfficiencyFixed_0_5.zip</em>, the <span class="math-tex">\(\beta\)</span> = 0.5 model (C) </li> <li><em>massTransferEfficiencyFixed_0_75.zip,</em> the <span class="math-tex">\(\beta\)</span> = 0.75 model (D)</li> <li><em>unstableCaseBB.zip, </em>the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li><em>alpha0_1 zip</em>, the <span class="math-tex">\(\alpha = 0.1\)</span> model (G) </li> <li><em>alpha0_5.zip</em>, the <span class="math-tex">\(\alpha = 0.5\)</span> model (H) </li> <li><em>alpha2_0.zip</em>, the <span class="math-tex">\(\alpha = 2.0\)</span> model (I) </li> <li><em>alpha10_0.zip</em>, the <span class="math-tex">\(\alpha = 10.0\)</span> model (J) </li> <li><em>rapid.zip</em>, the rapid SN model (L) </li> <li><em>maxNSmass2_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\)</span> model (M) </li> <li><em>maxNSmass3_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\)</span> model (N)</li> <li><em>noPISN.zip</em>, the no PISN model (O) </li> <li><em>ccSNkick_100km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 100 km/s model (P) </li> <li><em>ccSNkick_30km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 30 km/s model (Q)</li> <li> <em>noBHkick.zip, </em>the no BH SN kick model (R)</li> <li><em>wolf_rayet_multiplier_0_1.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 0.1\)</span> (S)</li> <li><em>wolf_rayet_multiplier_5.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 5\)</span> (T)<br> <br> </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates: <ul> <li><strong>csvFilesForFigure1_DCOpaper.zip </strong># contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li><strong>csvFilesForFigure2_and_3_DCOpaper.zip </strong># contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names: <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Details of how to use the data (a readme), as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a> </p> <p>If you use this data, please cite </p> <p>Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a></p>
BHNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers
<p>The data for all <strong>BHNS </strong>simulations shown in<em><strong> "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers". </strong>Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)</em></p> <p> </p> <p><strong>Contents: </strong></p> <ul> <li><strong>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E) and "unstable case BB + optimistic CE" (model F) files.</strong><br> <strong>These zip files are: </strong> <ul> <li><em>fiducial.zip, </em> the Fiducial model (A) and Optimistic CE model (K)</li> <li><em>massTransferEfficiencyFixed_0_25.zip, </em>the <span class="math-tex">\(\beta\)</span> = 0.25 model (B) </li> <li><em>massTransferEfficiencyFixed_0_5.zip</em>, the <span class="math-tex">\(\beta\)</span> = 0.5 model (C) </li> <li><em>massTransferEfficiencyFixed_0_75.zip,</em> the <span class="math-tex">\(\beta\)</span> = 0.75 model (D)</li> <li><em>unstableCaseBB.zip, </em>the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li><em>alpha0_1 zip</em>, the <span class="math-tex">\(\alpha = 0.1\)</span> model (G) </li> <li><em>alpha0_5.zip</em>, the <span class="math-tex">\(\alpha = 0.5\)</span> model (H) </li> <li><em>alpha2_0.zip</em>, the <span class="math-tex">\(\alpha = 2.0\)</span> model (I) </li> <li><em>alpha10_0.zip</em>, the <span class="math-tex">\(\alpha = 10.0\)</span> model (J) </li> <li><em>rapid.zip</em>, the rapid SN model (L) </li> <li><em>maxNSmass2_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\)</span> model (M) </li> <li><em>maxNSmass3_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\)</span> model (N)</li> <li><em>noPISN.zip</em>, the no PISN model (O) </li> <li><em>ccSNkick_100km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 100 km/s model (P) </li> <li><em>ccSNkick_30km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 30 km/s model (Q)</li> <li> <em>noBHkick.zip, </em>the no BH SN kick model (R)</li> <li><em>wolf_rayet_multiplier_0_1.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 0.1\)</span> (S)</li> <li><em>wolf_rayet_multiplier_5.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 5\)</span> (T)<br> <br> </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates: <ul> <li><strong>csvFilesForFigure1_DCOpaper.zip </strong># contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li><strong>csvFilesForFigure2_and_3_DCOpaper.zip </strong># contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names: <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Details of how to use the data (a readme), as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a> </p> <p>If you use this data, please cite </p> <p>Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a></p>
Daset from the paper "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"
<p>This repository contains the dataset produced by the population-synthesis code SEVN for the paper:<br> "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"</p> <p>In this paper, we exploit the SEVN code (publicly available at <a href="https://gitlab.com/sevncodes/sevn">https://gitlab.com/sevncodes/sevn</a>) to analyse the formation and properties of binary compact objects.</p> <p>The repository also includes the initial condistions used as input and the SEVN version used to run the simulations.</p> <p>#Content</p> <p>The repository contains the following folders:</p> <p>- data_from_simulations: the folder contains all the data produced by the simulations and used in the Iorio+22 paper<br> - InitialConditions: the folder contains all the intial conditions (and the code to generate them) that have been used for the Iorio+22 paper<br> - SEVN_iorio22: this folder contains the version of the SEVN code that has been used to run the simulations in the Iorio+22 paper</p> <p>Each folder contains a specific README with additional information</p> <p> </p> <p>#Contatcts</p> <p>giuliano.iorio.astro@gmail.com</p> <p> </p> <p> </p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Data behind the figures
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><br> <strong>Data behind the figures</strong></p> <p>This page contains the data behind various paper figures. The material for each figure is contained in a tar file. A short description can be found below. Figures not included here are associated with one of the other GWTC-3 data releases.</p> <p> </p> <p><strong>Figure 1</strong></p> <ul> <li>Figure01.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 1. This shows the number of candidates with probability of astrophysical origin > 50% as a function of surveyed time–volume.</p> <p>The dates for the first observing run (<a href="https://doi.org/10.1103/PhysRevX.6.041015">O1</a>) and second observing run (<a href="https://doi.org/10.1103/PhysRevX.9.031040">O2</a>) candidates are hard-coded into the script, and the dates for third observing run (<a href="https://arxiv.org/abs/2108.01045">O3a</a> and O3b) candidates are included in two text files. The effective binary neutron star time–volume (BNS-VT) for each observing run is stored in separate .csv files.</p> <p>Each .csv file contains two columns, the first is the GPS time and the second is the cumulative effective BNS VT in Mpc<sup>3</sup> kyr (this is converted to Gpc<sup>3</sup> yr in the included script).</p> <p>The included script reproduces Figure 1 from GWTC-3 using the supplied data.</p> <p> </p> <p><strong>Figure 2</strong></p> <ul> <li>Figure02.tar.gz</li> </ul> <p>Data and plotting scripts for GWTC-3: Figure 2. The figure shows sensitivity curves for LIGO Hanford, LIGO Livingston, and Virgo.</p> <p>The Python script reads the .txt files containing strain data for Hanford, Livingston, and Virgo and saves figures as PDF files.</p> <p>The sensitivity curves are representative of performance during O3b. Further examples of sensitivity curves across observing runs can be found from the <a href="https://www.gw-openscience.org/detector_status/">Gravitational Wave Open Science Center</a>.</p> <p> </p> <p><strong>Figure 3</strong></p> <ul> <li>Figure03.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 3. The left panel shows the <a href="https://doi.org/10.1088/1361-6382/abd594">binary neutron star inspiral range</a> of LIGO Hanford, LIGO Livingston, and Virgo versus time. The right panel shows histograms of the binary neutron star ranges for LIGO Hanford, LIGO Livingston, and Virgo.</p> <p>The Python script (figure_3.py) reads the range.txt files and the histogram.txt files to produce each panel and saves them as PDF files.</p> <p>Further summary information about the O3b run can be obtained from the <a href="https://www.gw-openscience.org/detector_status/O3b/">Gravitational Wave open Science Center</a>.</p> <p> </p> <p><strong>Figure 4</strong></p> <ul> <li>Figure04.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 4. This plot shows the rate of <a href="https://doi.org/10.1088/1361-6382/abfd85">non-Gaussian noise transients (glitches)</a> in the LIGO Hanford, LIGO Livingston and Virgo data across O3b. The recorded glitches are identified by the <a href="https://virgo.docs.ligo.org/virgoapp/Omicron/">Omicron</a> pipeline with signal-to-noise ratio of > 6.5. There is a reduction in the LIGO glitch rate after the introduction of <a href="https://doi.org/10.1088/1361-6382/abc906">reaction chain (RC) tracking</a>, which reduced the incidence of scattered light (slow scattering) glitches.</p> <p>The script glitch_rates_GWTC-3_Fig_4.py produces Figure 4 of GWTC-3 making use of the glitch rates stored in the glitch_rates_GWTC-3_Fig_4.h5 file. Run the script within an <a href="https://computing.docs.ligo.org/conda/environments/igwn-py37/">igwn-py37</a> or <a href="https://computing.docs.ligo.org/conda/environments/igwn-py38/">igwn-py38</a> Conda environment, paying attention to having the hdf5 file glitch_rates_GWTC-3_Fig_4.h5 in the same directory of the script. Pass the argument -v or --verbose for additional info about the rates.</p> <p> </p> <p><strong>Figure 5</strong></p> <ul> <li>Figure05.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 5. This figure illustrates the time–frequency structure of two common types of glitch seen in O3: <a href="https://doi.org/10.1088/1361-6382/abc906">slow scattering</a> and <a href="https://doi.org/10.1088/1361-6382/ac1ccb">fast scattering</a>. Both are caused by light scattering within the LIGO detectors.</p> <p>The Python script scattering_GWTC-3_Fig_5.py produces Figure 5 in the GWTC-3 Catalog paper using <a href="https://www.gw-openscience.org/O3/">open data</a>. The script saves the plot as a PDF file namely, scattering_GWTC-3_Fig_5.pdf and the data used to generate the plot in the files data_fast_scattering.txt and data_slow_scattering.txt.</p> <p>For further examples of the time–frequency structure of glitches, the community-science project <a href="https://gravityspy.org/">Gravity Spy</a> catalogs visualizations of glitches in gravitational-wave data.</p> <p> </p> <p><strong>Figure 12</strong></p> <ul> <li>Figure12.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 12. This plots results of the waveform consistency test (as does Figure 13), plotting the match between waveform templates and minimally modeled reconstructions. The on-source results are for the candidate signals, while the off-source results are for simulated signals with compatible properties.</p> <p>The waveform reconstructions are performed using <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> and <a href="https://gwburst.gitlab.io/">cWB</a>. The two pipelines select different sets of candidates to analyze.</p> <p>The Python script (figure_12.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB to produce the corresponding match–match plots (PDF files).</p> <p> </p> <p><strong>Figure 13</strong></p> <ul> <li>Figure13.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 13. This plots results of the waveform consistency test (as does Figure 12), plotting the p-values for the minimally modeled waveform reconstructions. The p-values are plotted in increasing order.</p> <p>The waveform reconstructions are performed using <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> and <a href="https://gwburst.gitlab.io/">cWB</a>. The two pipelines select different sets of candidates to analyze.</p> <p>The script (figure_13.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB (the same files used to produce Figure 12) to produce the corresponding p-value plots (PDF files).</p> <p> </p> <p><strong>Figure 14</strong></p> <ul> <li>Figure14.tar.gz</li> </ul> <p>Data and script to produce GWTC-3 Figure 14. This figure shows representative noise amplitude spectral densities for LIGO Hanford, LIGO Livingston, and Virgo during Observing Run 2 and Observing Run 3b.</p> <p>The script (figure_14.py) reads data from the .txt files included in the release to reproduce Figure 14 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 15</strong></p> <ul> <li>Figure15.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 15. This shows differences in the data used to analyze <a href="https://doi.org/10.7935/b024-1886">GW200115_042309</a>, with and without glitch subtraction. A low frequency cut (illustrated by the dotted white line) was used to remove the glitch in the <a href="https://doi.org/10.3847/2041-8213/ac082e">first analysis</a> of this candidate, whereas glitch subtraction is now used when performing parameter estimation. The curving orange line shows the approximate signal track.</p> <p>The script reads in the deglitched frame L-L1_HOFT_CLEAN_SUB60HZ_C01_T1700406_v4-1263095808-4096.gwf, downloaded from <a href="http://doi.org/10.5281/zenodo.5546679">an associated data release</a>, query raw <a href="https://www.gw-openscience.org/O3/">public LIGO Livingston data</a>, and reproduce Figure 15 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 16</strong></p> <ul> <li>Figure16.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 16. This figure illustrates the <a href="https://gwpy.github.io/docs/latest/examples/timeseries/qscan/">time–frequency structure</a> of data containing three O3 candidates identified only by <a href="https://gwburst.gitlab.io/">cWB</a> (the same as shown in Figure 17). Each shows evidence of instrumental origin. </p> <p>The script queries <a href="http://www.gw-openscience.org/O3/">public LIGO data</a> and reproduce Figure 16 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 17</strong></p> <ul> <li>Figure17.tar.gz</li> </ul> <p>Data and script for GWTC-3: Figure 17. This figure illustrates the <a href="https://doi.org/10.1088/1742-6596/363/1/012032">time–frequency structure</a> of candidate signals as reconstructed by <a href="https://gwburst.gitlab.io/">cWB</a> for three O3 candidates identified only by cWB (the same as shown in Figure 16). Each shows evidence of instrumental origin. For a compact binary coalescence signal, we would expect the signal to have a chirp structure, sweeping up from low to high frequencies.</p> <p>The script figs.py reads data (the reconstruction from cWB) from the .txt files to produce the corresponding time–frequency plots (PDF files). The script must be run three times to produce the panels of Figure 17: the event names are hardcoded into the script, which must be edited to produce the desired panel.</p> <p> </p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5571766 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data analysis, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p> <p> </p>
gwastro/1-ogc: v1.0 of the 1-OGC Catalog of Compact Binary Merger Candidates
<p>Introduction</p> <p>This repository contains the first Open Gravitational-wave Catalog (1-OGW), which is obtained by using the public data from Advanced LIGO's first observing run to search for compact-object binary mergers. Our analysis is based on new methods that improve the separation between signals and noise in matched-filter searches for gravitational waves from the merger of compact objects.</p> <p>We make available our complete catalog of events, including the sub-threshold population of candidates. The catalog contains approximately 150,000 candidate events. We note that since the vast majority of the events in the catalog are likely to be noise, we have provided information to rank and select candidate events. The three most significant signals in our catalog correspond to the binary black hole mergers <a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.061102">GW150914</a>, <a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.241103">GW151226</a>, and <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.93.122003">LVT151012</a>, respectively. We observe these signals at a true discovery rate of 99.92%. We find that LVT151012 alone has a 97.6% probability of being astrophysical in origin. No other significant binary black hole candidates are found, nor did we observe any significant binary neutron star or neutron star--black hole candidates.</p> <p>The catalog is stored in the file '1-OGC.hdf'. There are a variety of tools to access <a href="https://www.hdfgroup.org/">hdf files</a> from numerous computing languages. Here we will focus on access through python and <a href="www.h5py.org">h5py</a>.</p> <p>Analysis Details</p> <p>Details of the analysis are available in this <a href="https://arxiv.org/abs/1811.01921">preprint paper</a> and the configuration files needed to create the analysis workflows are provided in the <a href="https://github.com/gwastro/1-ogc/tree/master/workflow/configuration">workflow/configuration</a> directory.</p>
The OmegaWhite Survey for Short-period Variable Stars. V. Discovery of an Ultracompact Hot Subdwarf Binary with a Compact Companion in a 44-minute Orbit
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017ApJ...851...28K">Kupfer et al. (2017)</a>. MESA version 9793.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4357/aa9522">10.3847/1538-4357/aa9522</a></p>
ScienceDex guides
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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