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

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.