Walk 2019 Models

Models from L. Walk et al., Phys. Rev. D 101:123013, 2019 (s15.0c) taken from the Garching Supernova archive (https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Walk2019/) with permission for use in SNEWPY.

Reference: L. Walk et al., Neutrino emission characteristics of black hole formation in three-dimensional simulations of stellar collapse, Phys. Rev. D 101:123013, 2019

• doi:10.1103/PhysRevD.101.123013

• arXiv:1910.12971

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

from astropy import units as u

from snewpy.neutrino import Flavor, MassHierarchy
from snewpy.models.ccsn import Walk_2019
from snewpy.flavor_transformation import NoTransformation, AdiabaticMSW, ThreeFlavorDecoherence

mpl.rc('font', size=16)
%matplotlib inline

Initialize Models

To start, let’s see what progenitors are available for the Walk_2019 model. We can use the param property to view all physics parameters and their possible values. However, for this model, not all combinations of these parameters are valid, so we use the get_param_combinations function to get a list of all valid combinations:

# Walk_2019.param
Walk_2019.get_param_combinations()

[{'progenitor_mass': <Quantity 40. solMass>, 'direction': 1, 'eos': 'LS220'},
 {'progenitor_mass': <Quantity 40. solMass>, 'direction': 2, 'eos': 'LS220'},
 {'progenitor_mass': <Quantity 40. solMass>, 'direction': 3, 'eos': 'LS220'},
 {'progenitor_mass': <Quantity 75. solMass>, 'direction': 1, 'eos': 'LS220'},
 {'progenitor_mass': <Quantity 75. solMass>, 'direction': 2, 'eos': 'LS220'}]

We’ll initialise this progenitor. If this is the first time you’re using a progenitor, snewpy will automatically download the required data files for you.

model = Walk_2019(progenitor_mass=40*u.solMass, direction=1)

model

Walk_2019 Model

Parameter	Value
EOS	LS220
Progenitor mass	\(40\) \(\mathrm{M_{\odot}}\)
Direction	1

Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the Walk_2019 simulations didn’t distinguish between \(\nu_x\) and \(\bar{\nu}_x\), so both have the same luminosity.)

fig, axes = plt.subplots(1, 1, figsize=(12, 5), sharex=True, sharey=True, tight_layout=True)

for flavor in Flavor:
    axes.plot(model.time, model.luminosity[flavor]/1e51,  # Report luminosity in units foe/s
            label=flavor.to_tex(),
            color='C0' if flavor.is_electron else 'C1',
            ls='-' if flavor.is_neutrino else ':',
            lw=2)
axes.set(xlim=(0, 0.6),
        xlabel=r'$t-t_{\rm bounce}$ [s]',
        title=r'{}: {} $M_\odot$'.format(model.metadata['EOS'], model.metadata['Progenitor mass'].value))
axes.grid()
axes.legend(loc='upper right', ncol=2, fontsize=18)
axes.set(ylabel=r'luminosity [foe s$^{-1}$]');

Initial and Oscillated Spectra

Plot the neutrino spectra at the source and after the requested flavor transformation has been applied.

Adiabatic MSW Flavor Transformation: Normal mass ordering

# Adiabatic MSW effect. NMO is used by default.
xform_nmo = AdiabaticMSW()

# Energy array and time to compute spectra.
# Note that any convenient units can be used and the calculation will remain internally consistent.
E = np.linspace(0,100,201) * u.MeV
t = 50*u.ms

ispec = model.get_initial_spectra(t, E)
ospec_nmo = model.get_transformed_spectra(t, E, xform_nmo)

fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)

for i, spec in enumerate([ispec, ospec_nmo]):
    ax = axes[i]
    for flavor in Flavor:
        ax.plot(E, spec[flavor],
                label=flavor.to_tex(),
                color='C0' if flavor.is_electron else 'C1',
                ls='-' if flavor.is_neutrino else ':', lw=2,
                alpha=0.7)

    ax.set(xlabel=r'$E$ [{}]'.format(E.unit),
           title='Initial Spectra: $t = ${:.1f}'.format(t) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(t))
    ax.grid()
    ax.legend(loc='upper right', ncol=2, fontsize=16)

ax = axes[0]
ax.set(ylabel=r'flux [s$^{-1}$]')

fig.tight_layout();