In [1]:
import matplotlib.pylab as plt
%matplotlib inline
import numpy as np
import dm_models as dm
from time import time
In [2]:
target_atom = dm.AGe
massDM = 10.0 # GeV
sigma_n = 7e-42
efficiency = lambda x: 1.0
model = 'shm'
day = 1 # Jan 1.
# This function returns both Eee and dEr/dEee, which is needed for the conversion
def Ge_quenching_factor(ER):
Eee = dm.quench_keVr_to_keVee(ER)
dER_dEee = dm.quench_dEr_dEee(ER)
return Eee,dER_dEee
# Use this in a lambda function, so we can pass it around.
qfactor = lambda x: Ge_quenching_factor(x)
ER = np.linspace(1,8,1000)
dR,dR_dEee,Eee = dm.wimp_Er_day(ER,day=day,quenching_factor=qfactor,target_atom=target_atom,massDM=massDM,sigma_n=sigma_n,model=model)
In [3]:
plt.figure(figsize=(16,5))
plt.subplot(1,2,1)
plt.plot(ER,dR)
plt.ylabel(r'$dR/dE_R$',fontsize=24)
plt.xlabel(r'$E_R (keV)$',fontsize=24)
plt.subplot(1,2,2)
plt.plot(Eee,dR_dEee)
plt.ylabel(r'$dR/dE_{ee}$',fontsize=24)
plt.xlabel(r'$E_{ee} (keV)$',fontsize=24)
Out[3]:
In [4]:
# Plot effect of quenching factor
plt.plot(ER,Eee)
plt.xlabel(r'$E_R$ (keV)',fontsize=24)
plt.ylabel(r'$E_{ee}$ (keV)',fontsize=24)
Out[4]:
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