DOI: 10.5281/zenodo.13679313
title: Dataset: Asymptotic-state prediction for fast flavor transformation in neutron star mergers

description: 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:

In the datasets, spacetime components are generally ordered as:0 = x1 = y2 = z3 = t

Neutrino/antineutrino indices are ordered as:0 = neutrino1 = antineutrino

Flavor indices are ordered as:0 = e1 = mu2 = tau

#==========## Emu_data ##==========#Contains four files, each containing the initial and final results of simulations of neutrino quantum kinetics.

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).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)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)random: randomly generated initial conditions as described in the manuscript.

Contents:

F4_initial(1|ccm):[simulation index, 4 spacetime components, 2 neutrino/antineutrino, 3 flavors]The initial number density four-flux of each species in units of 1/cm^3.

F4_final(1|ccm):[simulation index, 4 spacetime components, 2 neutrino/antineutrino, 3 flavors]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.

F4_final_stddev(1|ccm):[simulation index, 4 spacetime components, 2 neutrino/antineutrino, 3 flavors]Not used in the manuscript. The standard deviation from the distribution of F4 that the final answer is averaged over.

directorynames:[simulation index]A string for each simulation index indicating the directory the calculation corresponds to. Used in debugging.

growthRate(1|s):[simulation index]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.

nf:scalarAssumed number of flavors. Should be 3 for all datasets included here.

xplot:[simulation index, time index]t/t_saturation. Used in creating Figure 2 of the manuscript.

y0plot:[simulation index, time index]N_ee(t) / N_ee(0). Used in creating Figure 2 of the manuscript.

y1plot:[simulation index, time index]N_offdiag_mag(t) / Ntot. Used in creating Figure 2 of the manuscript.

#===========## SpEC_data ##===========#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:

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]

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]

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.

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]

{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]

rho(g|ccm) - background comoving matter density in units of g/cm^3. Indexed by spatial position of the grid cell: [i,j,k]

T(MeV) - background comoving matter temperature in units of MeV.  Indexed by spatial position of the grid cell: [i,j,k]

Ye - background electron fraction (dimensionless).  Indexed by spatial position of the grid cell: [i,j,k]

fluxfac_{e,a,x} - flux factor of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}. Dimensionless.

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]a = gamma**2 + alpha**2b = -2. * gamma * etac = eta**2 - alpha**2discriminant = (b**2 - 4.*a*c) / (2.*a)**2hasCrossing = (discriminant >= 0)

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]

{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]

g{xx,yy,zz} - diagonal components of the metric tensor (dimensionless). Indexed by spatial position of the grid cell: [i,j,k]

#===========## ML_models ##===========#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.

Expected Rhea code:Snapshot at 10.5281/zenodo.13675320https://github.com/srichers/Rhea commit 68b7dc69d8fa33c7f5de1d8bec0790496f282d80