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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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 . 2020
License: CC BY
Data sources: Datacite
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Axisymmetric models for neutron star merger remnants with realistic thermal and rotational profiles: dataset

Authors: Camelio, Giovanni; Dietrich, Tim; Rosswog, Stephan; Haskell, Brynmor;

Axisymmetric models for neutron star merger remnants with realistic thermal and rotational profiles: dataset

Abstract

Dataset containing the results of the parameter space exploration of binary neutron star merger remnants and 12 selected models: * `search_results.dat` contains the parameters and properties of the successful results of the study. * `model_*.log` are the logs with settings, parameters, and properties of the selected models. * `model_*.out` are the profiles of the selected models in binary format. * `XNS_reader.py` is a python script to read the binary format, convert it to text, and compute some derived and global quantities. EDIT 2022-05-30: the output file in binary format does contain the profiles of temperature and entropy per baryon, but those are not outputted in the converted text file. You can manually modify the python script in order to output these profiles too. * `properties.csv` is a summary of the parameters and properties of the selected models. This dataset has been obtained with the stationary code XNS in General Relativity with the Conformal Flatness Approximation [Bucciantini and Del Zanna 2011; Pili et al. 2014; Camelio et al. 2018 and 2019]. The EOS is implemented as a cold piecewise polytrope [Read et al. 2009] plus a thermal gamma law. The models have been selected between those obtained in the parameter space exploration. For details see the companion paper [Camelio et al. 2021, PRD 103:063014]. If you use this dataset, please cite its Zenodo DOI and the companion paper [Camelio et al. 2021, PRD 103:063014]. EDIT 2022-05-30: an updated version of the code that has been used to produce this dataset is now on Zenodo (https://doi.org/10.5281/zenodo.6594069). This updated version is called ASWNS code, and it does not contain the model of binary neutron star merger remnant used for this dataset, but an older version of the model of nonbarotropic neutron star (from Camelio et al. 2019). You can implement any neutron star model on top of ASWNS, as shown in the examples provided with ASWNS.

GC and BH acknowledge support from the National Science Center Poland (NCN) via OPUS grant number 2019/33/B/ST9/00942. SR has been supported by the Swedish Research Council (VR) under grant number 2016-03657_3, by the Swedish National Space Board under grant number Dnr. 107/16, the research environment grant "Gravitational Radiation and Electromagnetic Astrophysical Transients (GREAT)" funded by the Swedish Research council (VR) under Dnr 2016-06012 and by the Knut and Alice Wallenberg Foundation under Dnr 2019.0112. BH acknowledges support from the National Science Center Poland (NCN) via SONATA BIS Grant No. 2015/18/E/ST9/00577.

{"references": ["Camelio, Dietrich, Rosswog, and Haskell 2021, \"Axisymmetric models for neutron star merger remnants with realistic thermal and rotational profiles\", PRD 103:063014, DOI:10.1103/PhysRevD.103.063014", "Bucciantini and Del Zanna 2011, DOI:10.1051/0004-6361/201015945", "Pili et al. 2014, DOI:10.1093/mnras/stu215", "Read et al. 2009, DOI:10.1103/PhysRevD.79.124032", "Camelio et al. 2018, DOI:10.1093/mnras/sty2181", "Camelio et al. 2019, DOI:10.1103/PhysRevD.100.123001", "Giovanni Camelio. (2022). ASWNS. Zenodo. https://doi.org/10.5281/zenodo.6594069"]}

Keywords

binary neutron star merger remnant, astrophysics

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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.
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