Catalogues of LISA-detectable sBHB inspirals and confusion backgrounds following the GWTC-3 fiducial model posterior
<h2>Data set</h2> <p>The contents of <code>SNR_min_2_z1_LISA_SNR.tar.gz</code> consist of approximately 10k folders, each corresponding to each of the samples of the public GWTC-3 fiducial sBHB population model posterior, and containing the following files:</p> <ul> <li><code>population.yaml</code>: population parameters of this sample.</li> <li><code>background.txt</code>: frequencies and characteristic strain squared of the sBHB confusion noise in the LISA band for this population.</li> <li><code>population_detector_frame_SNR.h5</code>: subset of loud sBHB sources, including but not limited to those with LISA SNR larger than 4 (missing in some samples). </li> </ul> <p>For a description of the population parameters in <code>population.yaml</code> and the individual source parameters in <code>population_detector_frame_SNR.h5</code>, see the contents of the <code>Demo.ipynb</code> notebook.</p> <p>To be able to run the notebooks described below, uncompress the <code>SNR_min_2_z1_LISA_SNR.tar.gz</code> file inside a <code>data/</code> subfolder under that of the notebook.</p> <h2>Demo notebook</h2> <p>For examples of how to load and process the catalogues, see the <code>Demo.ipynb</code> notebook.</p> <p>This notebook requires the following Python packages:</p> <p> <code>numpy, scipy, pandas, matplotlib, pyyaml, tqdm</code><br> <br>Some of the plots in the notebook require the <a href="https://github.com/JesusTorrado/extrapops" target="_blank" rel="noopener">extrapops</a> simulation package:</p> <p> <code>$ git clone git@github.com:JesusTorrado/extrapops.git</code><br> <code>$ cd extrapops</code><br> <code>$ pip install .</code></p> <h2>References</h2> <p>For detailed descriptions of the generation of the data set, see the references mentioned in the Zenodo page.</p> <h2>Questions and comments</h2> <p>Please use the <a href="https://github.com/JesusTorrado/LISA_sBHB_catalogues/issues">GitHub issue tracker</a> or contact the corresponding authors of the papers cited under the "described by" header of the Zenodo entry.</p>
ShareScore
36/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 4