Bollig 2016 Models

Data from Mirizzi et al., particular the models that go into Figure 17. Models (s11.2c and s27.0c) taken from the Garching Supernova archive (https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Bollig2016/) with permission for use in SNEWS2.0.

Reference: Mirizzi et al. Rivista del Nuovo Cimento Vol 39 N. 1-2 (2016)

• doi:10.1393/ncr/i2016-10120-8

• arXiv:1508.00785

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 Bollig_2016
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 Bollig_2016 model. We can use the param property to view all physics parameters and their possible values:

Bollig_2016.param

{'progenitor_mass': <Quantity [11.2, 27. ] solMass>, 'eos': ['LS220']}

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

m11 = Bollig_2016(progenitor_mass=11.2*u.solMass)
m27 = Bollig_2016(progenitor_mass=27*u.solMass)

m11

Bollig_2016 Model

Parameter	Value
EOS	LS220
Progenitor mass	\(11.2\) \(\mathrm{M_{\odot}}\)

Finally, let’s plot the luminosity of different neutrino flavors for this model. (Note that the Bollig_2016 simulations didn’t distinguish between \(\nu_x\) and \(\bar{\nu}_x\), so both have the same luminosity.)

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

for i, model in enumerate([m11, m27]):
    ax = axes[i]
    for flavor in Flavor:
        ax.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)
    ax.set(xlim=(0.0, 0.35),
           xlabel=r'$t-t_{\rm bounce}$ [s]',
           title=r'{}: {} $M_\odot$'.format(model.metadata['EOS'], model.metadata['Progenitor mass'].value))
    ax.grid()
    ax.legend(loc='upper right', ncol=2, fontsize=18)

axes[0].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 [erg$^{-1}$ s$^{-1}$]')

fig.tight_layout();