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104 results for “stars: neutron”
Binary neutron-star simulation SXS:NSNS:0002
Simulation of a neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Black-hole neutron-star binary simulation SXS:BHNS:0001
Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Black-hole neutron-star binary simulation SXS:BHNS:0003
Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Black-hole neutron-star binary simulation SXS:BHNS:0004
Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Black-hole neutron-star binary simulation SXS:BHNS:0002
Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Black-hole neutron-star binary simulation SXS:BHNS:0007
Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
A three-parameter characterization of neutron stars' mass-radius relation and equation of state
<p>A dataset for the paper <a href="https://arxiv.org/abs/2404.17647" target="_blank" rel="noopener">A three-parameter characterization of neutron stars' mass-radius relation and equation of state</a></p> <p>See<strong><em> Readme</em></strong> file for details.</p> <p>We aslo provide the <a href="https://github.com/pshternin/unicorrns" target="_blank" rel="noopener">unicorrns</a> python package wich allows to employ the results of our paper in a convenient way.</p> <p>The fitting subroutibe for NS mass and radius data analysis used in the paper is available at <a href="https://github.com/pshternin/nsmr2eos" target="_blank" rel="noopener">https://github.com/pshternin/nsmr2eos</a></p>
FRB Mock Catalog and Reproduction Package for "Birth and Evolution of Fast Radio Bursts: Strong Population-Based Evidence for a Neutron-Star Origin"
<h3>Quickstart: FRB Mock Catalog</h3> <p>A simulated 1-day catalog of one-off FRBs, that allows users to access the FRB population without installing the entire frbpoppy package. Download and unzip 1_Day_FRB_Sky_on_Earth.txt.zip (175 MB). This human and machine readable file contains 3.5E6 FRBs that are brighter than 0.01 Jy ms, the best limit in one-off FRB detection currently. The simulated catalog is produced by the perfect telescope in frbpoppy, free of selection effects, that observed 4pi of sky for 24 hrs, with minimum detectable fluence 0.01 Jy ms, for the best-fit no-delay SFR model. This file can be read using the accompanying jupyter notebook "starting_with_mock_catalog.ipynb".</p> <p>If you use this, please cite Wang & van Leeuwen 2024 (A&A), <a href="https://doi.org/10.1051/0004-6361/202450673">https://doi.org/10.1051/0004-6361/202450673</a></p> <h3>Reproduction package for the paper "Birth and Evolution of Fast Radio Bursts: Strong Population-Based Evidence for a Neutron-Star Origin"</h3> <p>ReproductionPackage.zip is a basic reproduction package for the paper "Birth and Evolution of Fast Radio Bursts: Strong Population-Based Evidence for a Neutron-Star Origin" by Wang & van Leeuwen (2024).</p> <p> * arXiv: [<a href="https://arxiv.org/abs/2405.06281">2405.06281</a>] <br> * DOI: [<a href="https://doi.org/10.1051/0004-6361/202450673">10.1051/0004-6361/202450673</a>] </p> <h3>Installation</h3> <p>First pull or download and `frbpoppy` from <https://github.com/TRASAL/frbpoppy>.<br>Then download `ReproductionPackage.zip` and extract it starting in the frbpoppy/ base directory.<br>The scripts to produce the Figures are found in folder `frbpoppy/tests/markov_chain_monte_carlo/`.<br>The data used for these Figures resides in folder `frbpoppy/data/populations/mcmc/`.</p> <h3>Software</h3> <p>The methods and software packages used to produce the results are listed in the paper (including links to the relevant publications and/or packages):<br> FRBPOPPY: <https://github.com/TRASAL/frbpoppy><br> TRASAL: <https://github.com/TRASAL></p> <h3>Raw Data</h3> <p>The data are publicly available at<br> https://www.wis-tns.org/</p> <p> </p>
Dataset: Asymptotic-state prediction for fast flavor transformation in neutron star mergers
<p>These data accompany the paper "Asymptotic-state predictions for fast flavor transformations in neutron star mergers" by S. Richers, J. Froustey, S. Ghosh, F. Foucart, and J. Gomez. The data are sorted into three directories:</p> <p>In the datasets, spacetime components are generally ordered as:<br>0 = x<br>1 = y<br>2 = z<br>3 = t</p> <p>Neutrino/antineutrino indices are ordered as:<br>0 = neutrino<br>1 = antineutrino</p> <p>Flavor indices are ordered as:<br>0 = e<br>1 = mu<br>2 = tau</p> <p>#==========#<br># Emu_data #<br>#==========#<br>Contains four files, each containing the initial and final results of simulations of neutrino quantum kinetics.</p> <p>M1NuLib-2016_5ms_rl0.h5: extracted from refinement level 0 of a 1.2Msun-1.2Msun NS merger simulation at 5ms after merger (https://doi.org/10.1103/PhysRevD.94.123016).<br>M1NuLib_3ms_rl1.h5: extracted from refinement level 1 of a 1.3Msun-1.4Msun NS merger at 3ms after merger (https://arxiv.org/abs/2407.15989)<br>M1NuLib_7ms_rl1.h5: extracted from refinement level 1 of a 1.3Msun-1.4Msun NS merger at 7ms after merger (https://arxiv.org/abs/2407.15989)<br>random: randomly generated initial conditions as described in the manuscript.</p> <p>Contents:</p> <p>F4_initial(1|ccm):<br>[simulation index, 4 spacetime components, 2 neutrino/antineutrino, 3 flavors]<br>The initial number density four-flux of each species in units of 1/cm^3.</p> <p>F4_final(1|ccm):<br>[simulation index, 4 spacetime components, 2 neutrino/antineutrino, 3 flavors]<br>The final number density four-flux of each species in units of 1/cm^3, averaged in time following the procedure indicated in the accompanying paper.</p> <p>F4_final_stddev(1|ccm):<br>[simulation index, 4 spacetime components, 2 neutrino/antineutrino, 3 flavors]<br>Not used in the manuscript. The standard deviation from the distribution of F4 that the final answer is averaged over.</p> <p>directorynames:<br>[simulation index]<br>A string for each simulation index indicating the directory the calculation corresponds to. Used in debugging.</p> <p>growthRate(1|s):<br>[simulation index]<br>Not used in the manuscript. Measured growth rate of the flavor off-diagonal components of the number density in s^-1. N_offdiag ~ e^(w t), where w is the growth rate and t is the time.</p> <p>nf:<br>scalar<br>Assumed number of flavors. Should be 3 for all datasets included here.</p> <p>xplot:<br>[simulation index, time index]<br>t/t_saturation. Used in creating Figure 2 of the manuscript.</p> <p>y0plot:<br>[simulation index, time index]<br>N_ee(t) / N_ee(0). Used in creating Figure 2 of the manuscript.</p> <p>y1plot:<br>[simulation index, time index]<br>N_offdiag_mag(t) / Ntot. Used in creating Figure 2 of the manuscript.</p> <p><br>#===========#<br># SpEC_data #<br>#===========#<br>Contains snapshots of grid quantities extracted from neutron star merger simulations. The file M1NuLib_2016_5ms_rl0.h5 contains data from the 1.2Msun-1.2Msun simulation of https://doi.org/10.1103/PhysRevD.94.123016. All others contain data from the 1.3Msun-1.4Msun M1-NuLib simulation of https://doi.org/10.48550/arXiv.2407.15989. The (3ms, 5ms, 7ms) part of the filename indicates how much time after merger the snapshot was taken. The (rl0, rl1, rl2, rl3) part of the filename indicates which refinement level the data are extracted from. NaNs in the data indicate that that cell was covered by a finer refinement level in the simulation. Radiation quantities are Lorentz transformed into an orthonormal tetrad comoving with the background fluid, and are rotated such that the net ELN flux is in the z direction. Each dataset contains:</p> <p>J_{e,a,x}(erg|ccm) - energy density of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}, where "x" contains the sum of all four heavy species, in units if erg/cm^3. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>fn_{e,a,x}(1|ccm) - number flux of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}, where "x" contains the sum of all four heavy species, in units if 1/cm^3. Indexed by direction and spatial position of the grid cell: [xyz, i,j,k]</p> <p>minerbo_Z{e,a,x} - the Z parameter of the maximum entropy closure at each point for {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}. Dimensionless.</p> <p>n_{e,a,x}(1|ccm) - number diensity of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos} in units of 1/cm^3. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>{x,y,z}(cm) - coordinates of the centers of each grid cell in units of cm. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>rho(g|ccm) - background comoving matter density in units of g/cm^3. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>T(MeV) - background comoving matter temperature in units of MeV. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>Ye - background electron fraction (dimensionless). Indexed by spatial position of the grid cell: [i,j,k]</p> <p>fluxfac_{e,a,x} - flux factor of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}. Dimensionless.</p> <p>crossing_discriminant - a crossing exists if this number is larger than 0. Computed using the following, based on Equation 16 in https://doi.org/10.1103/PhysRevD.106.083005. Indexed by spatial position of the grid cell: [i,j,k]<br>a = gamma**2 + alpha**2<br>b = -2. * gamma * eta<br>c = eta**2 - alpha**2<br>discriminant = (b**2 - 4.*a*c) / (2.*a)**2<br>hasCrossing = (discriminant >= 0)</p> <p>deltaCrossingAngle - width of the ELN crossing based on Equation 16 in https://doi.org/10.1103/PhysRevD.106.083005 in units of radians. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>{nue,anue}_absrate(1|s) - absorption rate for {electron neutrinos, electron antineutrinos} in units if 1/s. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>g{xx,yy,zz} - diagonal components of the metric tensor (dimensionless). Indexed by spatial position of the grid cell: [i,j,k]</p> <p><br>#===========#<br># ML_models #<br>#===========#<br>Contains the ML models used in the accompanying paper. In addition, there is an example script that uses the Rhea code to generate the relevant databases. Running the script requires the Rhea directory to be in the Python path. The Rhea code contains example Python and C++ code to use a trained model.</p> <p>Expected Rhea code:<br>Snapshot at 10.5281/zenodo.13675320<br>https://github.com/srichers/Rhea commit 68b7dc69d8fa33c7f5de1d8bec0790496f282d80</p> <p> </p>
General-Relativistic Hydrodynamics Simulation of a Neutron Star — Sub-Solar-Mass Black Hole Merger - 3D Density Visualization
<p>Matter density distribution of our simulation of a neutron star -- sub-solar mass black hole merger; cf. color bar to obtain a density estimate. The gray region represents the apparent horizon of the black hole.</p> <p>Visualization: Ivan Markin (University of Potsdam); Data: Swami Vivekanandji Chaurasia (Stockholm University)</p> <p>Simulations for the project have been performed on the national supercomputer HPE Apollo Hawk at the High Performance Computing (HPC) Center Stuttgart (HLRS) under the grant number GWanalysis/44189, on the GCS Supercomputer SuperMUC NG at the Leibniz Supercomputing Centre (LRZ) [project pn29ba], and on the HPC systems Lise/Emmy of the North German Supercomputing Alliance (HLRN) [project bbp00049] for the final production runs. The particular simulation shown here has been run on HLRN.</p>
General-Relativistic Hydrodynamics Simulation of a Neutron Star — Sub-Solar-Mass Black Hole Merger - Kilonova Luminosity Evolution Visualization
<p>Evolution of luminosity maps as seen by observers from the pole with Θ = 0° and from the four angles Φ = 0°, Φ = 90°, Φ = 180° and Φ = 270° in the equatorial plane with Θ = 90°. The maps show the luminosity from each region of the ejecta integrated along the line of sight and are calculated in (6000 − 8000) Å band at 1 day after the merger.</p> <p>Visualization: Ivan Markin (University of Potsdam), Mattia Bulla (University of Ferrara); Data: Anna Neuweiler (University of Potsdam), Swami Vivekanandji Chaurasia (Stockholm University)</p> <p>Simulations for the project have been performed on the national supercomputer HPE Apollo Hawk at the High Performance Computing (HPC) Center Stuttgart (HLRS) under the grant number GWanalysis/44189, on the GCS Supercomputer SuperMUC NG at the Leibniz Supercomputing Centre (LRZ) [project pn29ba], and on the HPC systems Lise/Emmy of the North German Supercomputing Alliance (HLRN) [project bbp00049] for the final production runs. The particular simulation shown here has been run on HLRN.</p>
Dataset for E. Grohs et al., Neutrino fast flavor instability in three dimensions for a neutron star merger, Physics Letters B, https://doi.org/10.1016/j.physletb.2023.138210
<p>.tgz file with hdf5 files for simulation data of a neutron star merger with neutrino flavor transformation. .h5 files are same information in plots 2 and 4 of https://doi.org/10.1016/j.physletb.2023.138210</p>
Binary Neutron Star Mergers: Mass Ejection, Electromagnetic Counterparts, and Nucleosynthesis
<p>We release dynamical ejecta data from binary neutron star merger simulations. The outflows are extracted at a fixed coordinate sphere with radius 300 G/c^2 Msun (= 443 km). Only material unbound according to the geodesic criterion is considered to be part of the dynamical ejecta. See [1] for more details.</p> <p>Included data:</p> <ul> <li>`Table2.txt`: Table 2 of the paper in machine readable format</li> <li>`tabulated_nucsyn.h5`: nucleosynthesis yields from pre-computed parametrized trajectories. The first three indices of each dataset are Ye, entropy, and expansion timescale tau. For example `Y_final[iYe, ientr, itau, iiso]` gives the final abundance of isotope `iiso` with `A[iiso]` and `Z[iiso]` for a trajectory with initial Ye = `Ye[iYe]`, initial entropy `s[ientr]`, and expansion timescale `tau[itau]`.</li> <li>`tabulated_rho.h5`: gives the density at T = 6 GK corresponding to the Ye, entropy, and expansion timescale used in `tabulated_nucsyn.h5`.</li> <li>`[model].tar`: ejecta data for individual simulations. The naming convention is the same as in the paper.</li> </ul> <p>For each model we provide:</p> <ul> <li>`outflow.txt`: angle integrated outflow rate and cumulated ejecta mass. Data are given in units with Msun = G = c = 1 (eg, the conversion factor for time to seconds is 4.9258e-6).</li> <li>`hist_entropy.dat`: histogram of the ejecta as a function of the entropy (in kb)</li> <li>`hist_vinf.dat`: histogram of the ejecta as a function of the asymptotic velocity (in units of c)</li> <li>`hist_ye.dat`: histogram of the ejecta as a function of the electron fraction Ye.</li> <li>`profile.txt`: time integrated ejecta profiles as a function of the polar angle.</li> <li>`hist_vinf_theta.h5`: histograms of the ejecta as a function of the asymptotic velocity and the polar angle.</li> <li>`hist_ye_theta.h5`: histograms of the ejecta as a function of the asymptotic velocity and the polar angle.</li> <li>`hist_ye_entropy_tau.h5`: histograms of the ejecta as a function of Ye, entropy, and expansion timescale tau.</li> </ul> <p>Additionally we distribute:</p> <ul> <li>Initial data generated with LORENE and associated EOS tables.</li> <li>EOS tables used for the evolution</li> <li>Parameter file used for each simulation</li> </ul> <p>For the multidimensional histograms the indices are ordered as specified in the file name, ie the file `hist_ye_theta.h5` tabulates the ejecta mass as a function of Ye (first index) and polar angle theta (second index).</p> <p><br> [1] D. Radice, A. Perego, K. Hotokezaka, S. A. Fromm, S. Bernuzzi, and L. F. Roberts, <em>Binary Neutron Star Mergers: Mass Ejection, Electromagnetic Counterparts, and Nucleosynthesis</em>, <a href="https://dx.doi.org/10.3847/1538-4357/aaf054">ApJ 869:130 (2018)</a>, <a href="https://arxiv.org/abs/1809.11161">arXiv:1809.11161</a></p>
Reproduction package for the paper "A strongly changing accretion morphology during the outburst decay of the neutron star X-ray binary 4U 1608-52"
<p>This is a basic reproduction package for the paper "A strongly changing accretion morphology during the outburst decay of the neutron star X-ray binary 4U 1608-52" by J. van den Eijnden et al. (2020). It provides reduced data sets, simulation scripts, X-ray spectral fits, and plotting scripts to allow the reproduction of the work performed in this paper. It also lists software used and data archives containing the public observational data. </p> <p>An open access version of the paper can be found at <a href="https://arxiv.org/abs/2002.04003">https://arxiv.org/abs/2002.04003</a>.</p>
Particle tracer and horizon data of equal-mass, binary neutron stars simulations
<p>We provide the particle tracer and black hole (BH) horizon data obtained from two binary neutron star (BNS) simulations performed with IllinoisGRMHD.</p> <p>The first dataset, MissingLink_gammalaw_particle_data.tar.bz2, contains data from a magnetized, equal-mass BNS simulation of the missing link initial data. The equation of state (EOS) used was a simple gamma-law with Gamma=2.</p> <p>The second dataset, Magnetized_equalmass_BNS_LS220_particle_data.tar.bz2, contains data from a magnetized, equal-mass BNS simulation that employs an advanced, tabulated EOS—LS220.</p> <p>Both simulations have gone through inspiral, merger, and the formation of a remnant hypermassive neutron star that eventually collapses to a BH.</p>
5,000 accretion disk trajectories from a double neutron star merger from Sprouse et al. ApJ 962 79 (2024)
<p>This dataset contains 5,000 trajectories from the 1.2 second long accretion disk simulation (with central black hole) of Sprouse et al. ApJ 962 79 (2024). Individual trajectories are contained in a folder labeled "trace_{tid}" where {tid} is the unique name of the trajectory. Each of these folders contains three files.</p> <p>The "initx.dat" file:</p> <ul> <li>The "initx.dat" file provides the initial mass fraction of the ejected material at a prescribed starting temperature and density using the SFHo equation of state.</li> <li>The columns of the "initx.dat" file are: Z (proton number), A (mass number), and X (mass fraction).</li> <li>The last column, X, should sum to unity by definition.</li> </ul> <p>The "metadata.dat" file:</p> <ul> <li>The "metadata.dat" file provides additional information about the ejected material.</li> <li>This file defines the trace id {tid}, the ejected mass, the maximum temperature reach in the simulation, the starting time, temperature, and density used to intialize the "initx.dat" file. </li> <li>The ejected mass associated with the trajectory can be used for the relative weighting of each trajectory.</li> <li>Units of associated quantities are defined in the file header (first line).</li> </ul> <p>The "trajectory.dat" file:</p> <ul> <li>The "trajectory.dat" file provides the time-dependent evolution of temperature (T), density (rho), radius (R) and electron fraction (Ye).</li> <li>This is the primary peace of information needed to run a nucleosynthesis network in post processing to determine the resultant abundances.</li> <li>Units of associated quantities are defined in the file header (first line).</li> </ul>
Observing Runs for Binary Neutron Star and Neutron Star-Black Hole mergers in the HLVK-Configuration during O4, using 20 million CBC injections (July 2024 edition)
<p>We have conducted a simulation of the HLVK configuration deployed during the ongoing O4 run. This project supports the training of kilonova regression with machine learning processes, requiring thousands of BNS to pass the threshold cutoff. Here we have 17,009 BNS passing the SNR threshold, along with 3,148 NSBH and 121,718 BBH, from 20 million CBCs injected. The upper-lower limit between NS and BH is 3 sun masses.</p> <p>However, due to size constraints, this repository contains only the simulation data and sky maps of BNS and NSBH detections that have passed the cutoff threshold. For the complete dataset, including BBH, please refer to <a href="../doi/10.5281/zenodo.12693652">https://zenodo.org/doi/10.5281/zenodo.12693652</a> .</p>
Optimal neutron-star mass ranges to constrain the equation of state of nuclear matter with electromagnetic and gravitational-wave observations: Animated Fig. 1
<p>This repository includes the animated version of Fig. 1 in https://arxiv.org/abs/1905.04900.</p>
Black-hole neutron-star binary simulation SXS:BHNS:0009
Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Black-hole neutron-star binary simulation SXS:BHNS:0010
Simulation of a black-hole neutron-star binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.