Supernovae

Optical

Make use of the quadratically-modulated sigmoidal rise / exponential decay, cf Karpenka 2012 and references therein:

Class: simlightcurve.curves.modsigmoidexp.ModSigmoidExp

from __future__ import absolute_import

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

%matplotlib inline
seaborn.set_context('poster')
seaborn.set_style("darkgrid")

from simlightcurve.curves import ModSigmoidExp as Hump


hr = 60*60
decay_tau=1.*24*hr
rise_tau=decay_tau*0.3
t1_offset = decay_tau
sn0 = Hump(a=3, b=0,
            t1_minus_t0=t1_offset,
            rise_tau=rise_tau, decay_tau = decay_tau,
            t0=None
            )
sn1 = Hump( a=1, b=3e-10,
            t1_minus_t0=t1_offset,
            rise_tau=rise_tau, decay_tau = decay_tau,
            # t0 = 0.7*decay_tau
            t0=None
            )

tsteps = np.arange(-8*rise_tau, 8*decay_tau, 30)

fig, axes = plt.subplots(1,1)
fig.suptitle('SNe optical lightcurves', fontsize=36)
ax=axes
# ax.axvline(0, ls='--')
# ax.axvline(t1_offset, ls='--')
ax.set_xlabel('Time')
ax.set_ylabel('Flux')
ax.plot(tsteps, sn0(tsteps), label='SN0')
ax.plot(tsteps, sn1(tsteps), label='SN1')
ax.legend()

<matplotlib.legend.Legend at 0x7f8d417a6470>

Radio

Make use of the ‘minishell’ model, a product of factors including exponential decay and power law, following VAST memo #3, (Ryder 2010)

from simlightcurve.curves import Minishell

hr=3600.0
timespan = 10000.
tsteps = np.linspace(-1,timespan,24*hr)

afterglow = Minishell(k1=2.5e2, k2=1.38e2, k3=1.47e5,
                       beta=-1.5, delta1=-2.56, delta2=-2.69,
                       t0=None)

seaborn.set_palette('husl')
fig, axes = plt.subplots(1,1)
fig.suptitle('SNe radio lightcurve', fontsize=36)
lc = afterglow(tsteps)
axes.plot(tsteps, lc)
axes.set_xlabel('Time')
axes.set_ylabel('Flux')
axes.set_xscale('log')
axes.set_yscale('log')
axes.set_ylim(0.001,np.max(lc)+0.2)
axes.set_xlim(2,timespan)

(2, 10000.0)