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Dataset results
29 results for “Neutron star mergers”
Data release for "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors"
<p>We publish skymap files in fits format of the simulation in our work "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors". There are 68000 BNS events, and results of different negative latencies are zipped in different tar files. An example jupyter notebook for using the data is provided.</p> <p> </p> <p> </p>
Axisymmetric models for neutron star merger remnants with realistic thermal and rotational profiles: dataset
<p>Dataset containing the results of the parameter space exploration of binary neutron star merger remnants and 12 selected models:<br> * `search_results.dat` contains the parameters and properties of the successful results of the study.<br> * `model_*.log` are the logs with settings, parameters, and properties of the selected models.<br> * `model_*.out` are the profiles of the selected models in binary format.<br> * `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.<br> * `properties.csv` is a summary of the parameters and properties of the selected models.<br> <br> 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].<br> The EOS is implemented as a cold piecewise polytrope [Read et al. 2009] plus a thermal gamma law.<br> The models have been selected between those obtained in the parameter space exploration.<br> For details see the companion paper [Camelio et al. 2021, PRD 103:063014].<br> <br> If you use this dataset, please cite its Zenodo DOI and the companion paper [Camelio et al. 2021, PRD 103:063014].</p> <p>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).<br> 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).<br> You can implement any neutron star model on top of ASWNS, as shown in the examples provided with ASWNS.</p>
The One-Armed Spiral Instability in Neutron Star Mergers and its Detectability in Gravitational Waves
<p>We distribute complete gravitational-wave signals in the Advanced LIGO band (10 Hz - 8192 Hz) of the inspiral and merger of two neutron stars. These waveforms been constructed by hybridizing numerical-relativity data obtained with the WhiskyTHC code [1] with tidal effective-one-body waveforms [2,3]. More details on the procedure used to generate these waveforms are given in [4]. </p> <p>The waveforms are distributed as HDF5 files containing the amplitude and phase of the -2 spin-weighted spherical harmonics multipoles of the strain:</p> <p><span class="math-tex">\(( h_+ - \mathrm{i} h_\times )_{l,m} = \frac{A_{l,m}}{D_{\rm cm}} \exp(-\mathrm{i} \phi_{l,m} )\)</span></p> <p>where <span class="math-tex">\(D_{\rm cm}\)</span> is the distance in cm from the source.</p> <p>The data files include a machine readable "/metadata" group with:</p> <ul> <li>/metadata/EOS: name of the equation of state</li> <li>/metadata/M_{A|B}: mass in isolation of star A (or B) in grams</li> <li>/metadata/R_{A|B}: radius of star A (or B) in cm</li> <li>/metadata/k2T: tidal coupling constant of the binary (see [3])</li> <li>/metadata/kl_{A|B}: l=2,3,4 dimensionless Love numbers of star A (or B)</li> </ul> <p>We store amplitude and phase for multipoles modes up to l=4 as time series sampled at 16384 Hz.</p> <p>We make these waveforms freely available in the hope that they will be useful. We kindly ask you to cite [3] and [4] in any publication resulting from the use of these waveforms.</p> <p>---<br /> [1] http://www.tapir.caltech.edu/~david_e/whiskythc.html<br /> [2] https://eob.ihes.fr/<br /> [3] S. Bernuzzi, A. Nagar, T. Dietrich, T. Damour; Modeling the Dynamics of Tidally Interacting Binary Neutron Stars up to the Merger; Phys.Rev.Lett. 114 (2015) 16, 161103.<br /> [4] D. Radice, S. Bernuzzi, C. D. Ott; The One-Armed Spiral Instability in Neutron Star Mergers and its Detectability in Gravitational Waves; arXiv:1603.05726.</p>
How loud are neutron star mergers?
<p>We release neutron star merger waveforms computed using fully general relativistic simulations of equal and unequal-mass binaries drawn from the galactic population. The simulations employ finite-temperature microphysical equations of state (LS220, DD2, and SFHo) and neutrino cooling. Please, see</p> <p>http://arxiv.org/abs/1512.06397</p> <p>for details.</p> <p> </p> <p>Each tarball refers to a simulation and contains</p> <ul> <li>Curvature multipolar waveform <span class="math-tex">\(\psi^{(4)}_{\ell m}\)</span></li> <li>Metric multipolar waveform <span class="math-tex">\(h_{\ell m}\)</span></li> <li>Radiated energy and angular momentum</li> </ul> <p>Files:</p> <ul> <li><em>waveforms/Psi4_l?_m?_r200.txt </em> <ul> <li>Columns: <span class="math-tex">\(t,\ \Re{(\psi^{(4)}_{\ell m})},\ \Im{(\psi^{(4)}_{\ell m})} \)</span></li> </ul> </li> <li><em>waveforms/Rh_l?_m?_r200.txt</em> <ul> <li>Columns: <span class="math-tex">\(u/M,\ \Re{(h_{\ell m})}/M,\ \Im{(h_{\ell m})}/M,\ \Re{(\dot{h}_{\ell m})},\ \Im{(\dot{h}_{\ell m}}),\ M\omega_{\ell m},\ A_{\ell m}/M,\ \phi_{\ell m},\ t \)</span></li> </ul> </li> <li><em>waveforms/Ej_r200.txt</em> <ul> <li>Columns: <span class="math-tex">\(E_b,\ j,\ E_\text{rad},\ J_\text{rad},\ t \)</span></li> </ul> </li> </ul> <p>where</p> <ul> <li><span class="math-tex">\(t\)</span> simulation time</li> <li><span class="math-tex">\(u\)</span> retarded time</li> <li><span class="math-tex">\(M\)</span> binary mass</li> <li><span class="math-tex">\(\omega_{\ell m}\)</span> wave frequency</li> <li><span class="math-tex">\(A_{\ell m}\)</span> wave amplitude</li> <li><span class="math-tex">\(\phi_{\ell m}\)</span> wave phase</li> <li><span class="math-tex">\(E_\text{GW}\)</span> radiated energy</li> <li><span class="math-tex">\(J_\text{GW}\)</span> radiated angular momentum</li> <li><span class="math-tex">\(E_b\)</span> binary energy</li> <li><span class="math-tex">\(j\)</span> binary specific angular momentum</li> </ul> <p>Please refer to the paper and references therein for the definition of the different quantities.</p> <p>Units <span class="math-tex">\(c=G=M_\text{Sun}=1\)</span></p>
Reproduction package for the paper "Constraining a neutron star merger origin for localized fast radio bursts"
<p>This is a reproduction package for the paper <a href="https://academic.oup.com/mnras/article/497/3/3131/5875920">"Constraining a neutron star merger origin for localized fast radio bursts"</a> by Gourdji et al. (2020) and published in MNRAS. This package provides a Jupyter notebook and the necessary information to reproduce the figures and main results of this paper.</p>
Reproduction package for the paper "A search for radio emission from double-neutron star merger GW190425 using Apertif"
<p>This is a basic reproduction package for the paper "A search for radio emission from double-neutron star merger GW190425 using Apertif".</p>
NMMA: A nuclear-physics and multi-messenger astrophysics framework to analyze binary neutron star mergers
<p>Data release associated with the preprint "<em>NMMA: A nuclear-physics and multi-messenger astrophysics framework to analyze binary neutron star mergers</em>"</p> <p>Data includes:</p> <p>EOS files:</p> <ul> <li>5000 eos files with radius (km), mass (Msun), and tidal deformability as columns stored under eos/eos_data</li> <li>prior probabilities for the EOSs are stored in eos/eos_prior_probability.dat</li> </ul> <p>Posterior samples:</p> <ul> <li>Posterior samples based on the analysis of GW170817 and AT2017gfo stored in posterior_samples/GW170817-AT2017gfo_posterior_samples.dat</li> <li>Posterior samples based on the analysis of GW170817, AT2017gfo, and the afterglow of GRB170817A are stored in posterior_samples/GW170817-AT2017gfo-GRB170817A_afterglow_posterior_samples.dat</li> </ul> <p> </p>
Data Release: Population properties and multimessenger prospects of neutron star-black hole mergers following GWTC-3
<p>Neutron star-black hole (NSBH) mergers detected in gravitational waves have the potential to shed light on supernova physics, the dense matter equation of state, and the astrophysical processes that power their potential electromagnetic counterparts. We use the population of four candidate NSBH events detected in gravitational waves so far with a false alarm rate ≤1 yr−1 to constrain the mass and spin distributions and multimessenger prospects of these systems. We find that the black holes in NSBHs are both less massive and more slowly spinning than those in black hole binaries. We also find evidence for a mass gap between the most massive neutron stars and least massive black holes in NSBHs at 98.6% credibility. We consider both a Gaussian and a power-law pairing function for the distribution of the mass ratio between the neutron star and black hole masses but find no statistical preference between the two. Using an approach driven by gravitational-wave data rather than binary simulations, we find that fewer than 14% of NSBH mergers detectable in gravitational waves will have an electromagnetic counterpart. Finally, we propose a method for the multimessenger analysis of NSBH mergers based on the nondetection of an electromagnetic counterpart and conclude that, even in the most optimistic case, the constraints on the neutron star equation of state that can be obtained with multimessenger NSBH detections are not competitive with those from gravitational-wave measurements of tides in binary neutron star mergers and radio and X-ray pulsar observations.</p>
Dataset from: Multi-messenger observations of binary neutron star mergers in the O4 run
<p>The binary neutron stars population data from the paper <strong><em>"Multi-messenger observations of binary neutron star mergers in the O4 run" </em>(<a href="https://arxiv.org/abs/2204.07592">https://arxiv.org/abs/2204.07592</a>).</strong></p> <p>Details of how to use the data, as well as the scripts to reproduce the figures of the main text of the paper are given in the accompanying Github repository <a href="https://github.com/acolombo140/O4NSNS">https://github.com/acolombo140/O4NSNS</a></p> <p>If you use this data, please cite the above manuscript.</p> <p> </p>
Reproduction package for the paper "Investigating the detection rates and inference of gravitational-wave and radio emission from black hole-neutron star mergers"
<p>This is a basic reproduction package for the paper "Investigating the detection rates and inference of gravitational-wave and radio emission from black hole neutron star mergers".</p>
r-process abundances in neutron star merger dynamical ejecta given different fission yields
<p>This data release contains nucleosynthesis predictions following Vassh et al. (2020) for the r-process abundances of binary neutron star merger ejecta based on the simulation trajectories of Radice et al. (2018), which were mapped to parametrized trajectories based on their neutron-richness (Ye), entropy (s), and expansion timescale (tau). The neutron star merger scenarios considered here cover a wide range of neutron star masses. Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating. Results are reported for two fission yield sets, the Finite Range Liquid Drop Model (FRLDM, Mumpower et al. 2020) and 50/50 symmetric splits. All calculations assumed FRDM12 (Möller et al. 2016) for the nuclear mass model and apply the SFHO equation of state when obtaining the initial composition from nuclear statistical equilibrium (NSE).</p> <p>When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh 2022). Refer to Vassh et al. (2020) for further details on the nuclear physics inputs, to Mumpower et al. (2018) for further details on FRLDM, and to Radice et al. (2018) for further details on the merger ejecta trajectories.</p>
A candidate coherent radio flash following a neutron star merger
<p>This is a reproduction file for the paper ``A candidate coherent radio flash following a neutron star merger``. The sources of the raw data are provided, the link to the calibration and imaging pipeline used, and the scripts required to reproduce the results.</p>
Datasets for "Needle in a Bayes Stack: a Hierarchical Bayesian Method for Constraining the Neutron Star Equation of State with an Ensemble of Binary Neutron Star Post-merger Remnants"
<p>All data used for "Needle in a Bayes Stack: a Hierarchical Bayesian Method for Constraining the Neutron Star Equation of State with an Ensemble of Binary Neutron Star Post-merger Remnants", Criswell, A.W., et al. (2022). The code used to create the paper results from this data can be found at <a href="https://github.com/criswellalexander/hbpm_paper">https://github.com/criswellalexander/hbpm_paper</a> and the underlying software package can be found at <a href="https://github.com/criswellalexander/bayestack">https://github.com/criswellalexander/bayestack</a>.</p>
General-Relativistic Hydrodynamics Simulation of a Neutron Star — Sub-Solar-Mass Black Hole Merger - 3D Ejecta Data
<p>This dataset contains the 3D output for NSbh_R2 run at refinement level l=1 and simulation time t=7950 (t=39.16 ms).</p> <p>Data: Swami Vivekanandji Chaurasia (Stockholm University), Data release packaging: Ivan Markin (University of Potsdam);</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 has been run on HLRN.</p>
Equation of state for simulations of core-collapse supernovae and neutron-star mergers
<p>We construct a new equation of state (EOS) for numerical simulations of core-collapse supernovae and neutron-star mergers based on an extended relativistic mean-field model with a small symmetry energy slope L, which is compatible with both experimental nuclear data and recent observations of neutron stars. The new EOS table (EOS4) based on the extended TM1 (TM1e) model with L=40 MeV is designed in the same tabular form as the commonly used Shen EOS (EOS2) based on the original TM1 model with L=110.8 MeV. This is convenient and useful for performing numerical simulations and examining the influences of symmetry energy and its density dependence on astrophysical phenomena. </p> <p> </p>
Ultra-luminous X-ray sources and neutron-star-black-hole mergers from very massive close binaries at low metallicity
<p>MESA inlists, run_star_extras, and data associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017A&A...604A..55M">Marchant et al. (2017)</a>. MESA version 8118.</p> <p>Publication DOI: <a href="https://doi.org/10.1051/0004-6361/201630188">10.1051/0004-6361/201630188</a></p> <p>Files are also available in a gihub repository <a href="https://github.com/orlox/mesa_input_data/tree/master/2016_ULX">here</a></p> <p>Upload includes post-processed simulation output in the files Z-XX.tar.xz, where XX represents the metallicity (Z-25.tar.gz is for a metallicity of log10(Z)=-2.5). Each folder inside the archive corresponds to a single MESA simulation, with the name indicating the value of log10(M_1), q=M2/M1 and the orbital period in days. For example, the directory 1.600_0.500_0.900 corresponds to the simulation with log10(M1/Msun)=1.6, M2/M1=0.5 and Porb=0.9 days. Each folder is also a MESA template folder, containing all input files neccesary to reproduce that individual simulation.</p> <p>The file summary_tables.tar.gz contains summarized information for each simulation in ascii format.</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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.