Yields of 1e-5 and 1e-6 as a input for SYGMA can be calculated by scaling down the yields fromm Z=1e-4. SYGMA is doing it in the read_yields.py. You can find the documentation here.
Run this to set up the environment:
In [1]:
#mpld3.enable_notebook()
#%pylab
In [1]:
s1=s.sygma(iniZ=0.0001,dt=7e6,tend=1e9)
In [11]:
s1.history.isotopes[:10]
Out[11]:
In [12]:
s1.history.isotopes.index('Li-6')
In [13]:
len(s1.history.ism_iso_yield[0])
Out[13]:
In [10]:
np.array(s1.history.ism_iso_yield[0])/1e11
Out[10]:
Start the simulation by executing the following line. You create a single stellar population out of a gas cloud of $10^{11}M_{\odot}$ and a metallicity of $Z=10^{-4}$ ([Fe/H]=-2.3) and let it evolve over $10^{10}$ years. AGB, massive stars with SN2 as well as SN1a eject their material into the ISM.
In [2]:
s1=s.sygma(iniZ=0.0001,dt=7e6,tend=1e9)
s2=s.sygma(iniZ=0.00001,dt=7e6,tend=1e9)
s3=s.sygma(iniZ=0.000001,dt=7e6,tend=1e9)
$m_{eject,i}(Z) =(P_i-1.)*(X_{ini,set,i}*Y_{tot,eject}) + (X_{ini,Z,i}*Y_{tot,eject}) $
if $m_{eject,i}(Z) < 0$ : $m_{eject,i}(Z) = 0 $
$m_{eject,i}(Z) =(P_i-1.)*(X_{ini,Z,i}*Y_{tot,eject}) + (X_{ini,Z,i}*Y_{tot,eject}) $
Alter H for that: $m_{eject,H}(Z)$ is reduced
In [3]:
ele=['H','He','C','N','O','Mg','Ca','Ti','Fe','Co','Zn']
color=['r','b','g']
marker=['o','s','D']
label=['0.0001','0.00001','0.000001']
for e in range(len(ele)):
s1.plot_mass(fig=e,yaxis=ele[e],color=color[0],marker=marker[0],label=label[0])
s2.plot_mass(fig=e,yaxis=ele[e],color=color[1],marker=marker[1],label=label[1])
s3.plot_mass(fig=e,yaxis=ele[e],color=color[2],marker=marker[2],label=label[2])
plt.title(ele[e])
In [4]:
ele=['H','He','C','N','O','Mg','Ca','Ti','Fe','Co','Zn']
color=['r','b','g','k']
marker=['o','s','D','x']
label=['0.01','0.006','0.001','0.0001']
lim=[[1e9,1e10],[1e5,1e9],[1e4,1e8],[1e5,1e9],[1e4,1e8],[1e2,1e8],[1e1,1e7],[1e1,1e8],[1e1,1e8],[1e1,1e8]]
for e in range(len(ele)):
s1.plot_mass_range_contributions(fig=e,specie=ele[e],color='r',prodfac=False,label='0.0001')
s2.plot_mass_range_contributions(fig=e,specie=ele[e],color='g',prodfac=False,label='0.00001')
s3.plot_mass_range_contributions(fig=e,specie=ele[e],color='b',prodfac=False,label='0.000001')
plt.yscale('log')
#plt.ylim(lim[e][0],lim[e][1])
plt.title(ele[e])
N is different (sec), T strange metallicity trend
In [5]:
s00=s.sygma(iniZ=0.01,dt=7e6,tend=1e9)
s11=s.sygma(iniZ=0.006,dt=7e6,tend=1e9)
s22=s.sygma(iniZ=0.001,dt=7e6,tend=1e9)
s33=s.sygma(iniZ=0.0001,dt=7e6,tend=1e9)
In [6]:
ele=['H','C','N','O','Mg','Ca','Ti','Fe','Co','Zn']
color=['r','b','g','k']
marker=['o','s','D','x']
label=['0.01','0.006','0.001','0.0001']
for e in range(len(ele)):
s00.plot_mass(fig=e,yaxis=ele[e],color=color[0],marker=marker[0],label=label[0])
s11.plot_mass(fig=e,yaxis=ele[e],color=color[1],marker=marker[1],label=label[1])
s22.plot_mass(fig=e,yaxis=ele[e],color=color[2],marker=marker[2],label=label[2])
s33.plot_mass(fig=e,yaxis=ele[e],color=color[3],marker=marker[3],label=label[3])
plt.title(ele[e])
In [7]:
s00.plot_mass_range_contributions(specie='N',color='k',prodfac=False,label='0.01')
s11.plot_mass_range_contributions(specie='N',color='r',prodfac=False,label='0.006')
s22.plot_mass_range_contributions(specie='N',color='g',prodfac=False,label='0.001')
s33.plot_mass_range_contributions(specie='N',color='b',prodfac=False,label='0.0001')
plt.yscale('log')
plt.ylim(1e5,1e8)
Out[7]:
In [8]:
s00.plot_mass_range_contributions(specie='Zn',color='k',prodfac=False,label='0.01')
s11.plot_mass_range_contributions(specie='Zn',color='r',prodfac=False,label='0.006')
s22.plot_mass_range_contributions(specie='Zn',color='g',prodfac=False,label='0.001')
s33.plot_mass_range_contributions(specie='Zn',color='b',prodfac=False,label='0.0001')
plt.yscale('log')
plt.ylim(1e1,1e8)
Out[8]:
Looks secondary for massive stars and AGB stars! Where is Zn coming in the M12,M15 models? seems to be primary in thoses cases. Large difference in mass cut!
In [9]:
ele=['H','C','N','O','Mg','Ca','Ti','Fe','Co','Zn']
color=['r','b','g','k']
marker=['o','s','D','x']
label=['0.01','0.006','0.001','0.0001']
lim=[[1e9,1e10],[1e5,1e9],[1e4,1e8],[1e5,1e9],[1e4,1e8],[1e2,1e8],[1e1,1e7],[1e1,1e8],[1e1,1e8],[1e1,1e8]]
for e in range(len(ele)):
s00.plot_mass_range_contributions(fig=e,specie=ele[e],color='k',prodfac=False,label='0.01')
s11.plot_mass_range_contributions(fig=e,specie=ele[e],color='r',prodfac=False,label='0.006')
s22.plot_mass_range_contributions(fig=e,specie=ele[e],color='g',prodfac=False,label='0.001')
s33.plot_mass_range_contributions(fig=e,specie=ele[e],color='b',prodfac=False,label='0.0001')
plt.yscale('log')
plt.ylim(lim[e][0],lim[e][1])
plt.title(ele[e])
In [9]: