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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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Catalogues of LISA-detectable sBHB inspirals and confusion backgrounds following the GWTC-3 fiducial model posterior

Authors: Torrado, Jesus; Karnesis, Nikolaos; Pieroni, Mauro;

Catalogues of LISA-detectable sBHB inspirals and confusion backgrounds following the GWTC-3 fiducial model posterior

Abstract

Data set The contents of SNR_min_2_z1_LISA_SNR.tar.gz 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: population.yaml: population parameters of this sample. background.txt: frequencies and characteristic strain squared of the sBHB confusion noise in the LISA band for this population. population_detector_frame_SNR.h5: subset of loud sBHB sources, including but not limited to those with LISA SNR larger than 4 (missing in some samples). For a description of the population parameters in population.yaml and the individual source parameters in population_detector_frame_SNR.h5, see the contents of the Demo.ipynb notebook. To be able to run the notebooks described below, uncompress the SNR_min_2_z1_LISA_SNR.tar.gz file inside a data/ subfolder under that of the notebook. Demo notebook For examples of how to load and process the catalogues, see the Demo.ipynb notebook. This notebook requires the following Python packages: numpy, scipy, pandas, matplotlib, pyyaml, tqdm Some of the plots in the notebook require the extrapops simulation package: $ git clone git@github.com:JesusTorrado/extrapops.git $ cd extrapops $ pip install . References For detailed descriptions of the generation of the data set, see the references mentioned in the Zenodo page. Questions and comments Please use the GitHub issue tracker or contact the corresponding authors of the papers above.

Keywords

Black holes, Gravitational waves

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average