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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 &quot;<em><strong>Gravitational wave sources in our Galactic backyard: Predictions for BHBH, BHNS and NSNS binaries in LISA</strong></em>&quot;</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&#39;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&#39; as discussed in paper (now we allow HeHG donors to survive common-envelopes)</p> <ul> </ul>

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
16
Reuse readiness
8
Engagement
4

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