def genBands(
nSim=1e5,
maxS1=50,
f_drift=700,
g1=0.075,
SPE_res=0.5,
eff_extract=0.95,
SE_size=50,
SE_res=10,
e_lifetime=1000,
dt0=500,
minSpikePE=0.25,
min_NS1_coin=3,
min_Ne_ext=5,
Det="LZ",
S2raw_min=250,
xe_density=2.888,
):
# Setup the LUX or LZ detector:
pt = 0 # start with NR
NEST = libNEST.NEST(pt, 10, f_drift, xe_density) # 0 is NR, 1 is ER ... Energy, EField(V/cm), density
if Det == "LZ":
myDet = libNEST.Detector()
myDet.LZSettings()
NEST.SetDetectorParameters(myDet)
elif Det == "LUX":
myDet = libNEST.Detector()
myDet.LUXSettings()
NEST.SetDetectorParameters(myDet)
else:
print("Invalid detector, defulting to LZ")
myDet = libNEST.Detector()
myDet.LZSettings()
NEST.SetDetectorParameters(myDet)
# Calculate the NR band, and count below that for acceptance ########
maxEr = 100 # keVnr, for flat spectrum... DD
Flat_Er = maxEr * st.uniform.rvs(size=nSim)
# 0-100 keVnr
## Generate Signal in the detector ##
Nph = []
Ne = []
S1 = []
S2 = []
S1c = []
S2c = []
for en in Flat_Er:
NEST.SetEnergy(en)
NEST.DetectorResponse()
Nph.append(NEST.GetNumPhotons())
Ne.append(NEST.GetNumElectrons())
S1.append(NEST.GetS1())
S2.append(NEST.GetS2())
S1c.append(NEST.GetS1c())
S2c.append(NEST.GetS2c())
Nph = np.array(Nph)
Ne = np.array(Ne)
S1 = np.array(S1)
S2 = np.array(S2)
S1c = np.array(S1c)
S2c = np.array(S2c)
S1_bins = linspace(1, maxS1, maxS1)
S1_bin_cen_n = empty_like(S1_bins)
mean_S2oS1_n = empty_like(S1_bins)
std_S2oS1_n = empty_like(S1_bins)
# Find the NR S2/S1 band at each S1
det_cuts = (S1c > 0) & (S2c >= S2raw_min)
for index, S1s in enumerate(S1_bins):
cut = det_cuts & inrange(S1c, [S1s - 0.5, S1s + 0.5])
S1_bin_cen_n[index] = mean(S1c[cut])
mean_S2oS1_n[index] = mean(log10(S2c[cut] / S1c[cut]))
std_S2oS1_n[index] = std(log10(S2c[cut] / S1c[cut]))
# Calc Nr Efficiency vs E
E_bins = linspace(1, maxEr, maxEr)
E_bin_cen_n = empty_like(E_bins)
Eff_n = empty_like(E_bins)
det_cuts = (S1c > 0) & (S2c >= S2raw_min)
for index, Es in enumerate(E_bins):
cut = det_cuts & inrange(Flat_Er, [Es - 0.5, Es + 0.5])
E_bin_cen_n[index] = mean(Flat_Er[cut])
Eff_n[index] = sum(cut) / sum(inrange(Flat_Er, [Es - 0.5, Es + 0.5])) # cut/total_in_bin
# Calculate the ER band ####################################################
maxEe = 100 # keVee, for flat ER spectrum
Flat_Ee = maxEe * st.uniform.rvs(size=nSim)
# 0-100 keVee
## Generate Signal in the detector ##
Nph = []
Ne = []
S1 = []
S2 = []
S1c = []
S2c = []
NEST.SetParticleType(1) # set to ER events
for en in Flat_Er:
NEST.SetEnergy(en)
NEST.DetectorResponse()
Nph.append(NEST.GetNumPhotons())
Ne.append(NEST.GetNumElectrons())
S1.append(NEST.GetS1())
S2.append(NEST.GetS2())
S1c.append(NEST.GetS1c())
S2c.append(NEST.GetS2c())
Nph = np.array(Nph)
Ne = np.array(Ne)
S1 = np.array(S1)
S2 = np.array(S2)
S1c = np.array(S1c)
S2c = np.array(S2c)
S1_bins = linspace(1, maxS1, maxS1)
S1_bins = linspace(1, maxS1, maxS1)
S1_bin_cen_e = empty_like(S1_bins)
mean_S2oS1_e = empty_like(S1_bins)
stdev_S2oS1_e = empty_like(S1_bins)
# Find the ER S2/S1 band at each S1
det_cuts = (S1c > 0) & (S2c >= S2raw_min)
for index, S1s in enumerate(S1_bins):
cut = det_cuts & inrange(S1c, [S1s - 0.5, S1s + 0.5])
S1_bin_cen_e[index] = mean(S1c[cut])
mean_S2oS1_e[index] = mean(log10(S2c[cut] / S1c[cut]))
stdev_S2oS1_e[index] = std(log10(S2c[cut] / S1c[cut]))
# Calc Nr Efficiency vs E
E_bins = linspace(1, maxEe, maxEe)
E_bin_cen_e = empty_like(E_bins)
Eff_e = empty_like(E_bins)
det_cuts = (S1c > 0) & (S2c >= S2raw_min)
for index, Es in enumerate(E_bins):
cut = det_cuts & inrange(Flat_Ee, [Es - 0.5, Es + 0.5])
E_bin_cen_e[index] = mean(Flat_Ee[cut])
Eff_e[index] = sum(cut) / sum(inrange(Flat_Ee, [Es - 0.5, Es + 0.5])) # cut/total_in_bin
return (
S1_bin_cen_n,
mean_S2oS1_n,
std_S2oS1_n,
S1_bin_cen_e,
mean_S2oS1_e,
stdev_S2oS1_e,
E_bin_cen_e,
Eff_e,
E_bin_cen_n,
Eff_n,
)
def genBands(NEST=NEST_setup(),nSim=2e5, maxS1=50, S2raw_min=450, Ermin=0, mWmp=50):
'''input: NEST obj, nSim, maxS1, S2raw_min, Ermin, mWmp.
output: S1_bin_cen_n, mean_S2oS1_n, std_S2oS1_n, S1_bin_cen_e, mean_S2oS1_e, std_S2oS1_e, E_bin_cen_e, Eff_e, E_S1_e(average E for a given S1), E_bin_cen_n, Eff_n, E_S1_n(average E for a given S1), num_leak_e(vs S1 bin), num_total_e(vs S1 bin), leak_gauss_e(vs S1 bin), sNR(spline NR band) '''
#start with NR
NEST.SetParticleType(0)
maxEr=100 #keVnr, for flat spectrum
#Default, use a 50 GeV WIMP for discrimnation
if mWmp==-1:
Er = maxEr*st.uniform.rvs(size=nSim); #0-100 keVnr
else:
Er = rates.WIMP.genRandEnergies(nSim, mW=mWmp)
## Generate Signal in the detector ##
Nph=[]
Ne=[]
S1=[]
S2=[]
S1c=[]
S2c=[]
for en in Er:
NEST.SetEnergy(en)
NEST.DetectorResponse()
Nph.append(NEST.GetNumPhotons())
Ne.append(NEST.GetNumElectrons())
S1.append(NEST.GetS1())
S2.append(NEST.GetS2())
S1c.append(NEST.GetS1c())
S2c.append(NEST.GetS2c())
Nph=np.array(Nph)
Ne=np.array(Ne)
S1=np.array(S1)
S2=np.array(S2)
S1c=np.array(S1c)
S2c=np.array(S2c)
S1_bins=linspace(1,maxS1,maxS1)
S1_bin_cen_n=empty_like(S1_bins)
mean_S2oS1_n=empty_like(S1_bins)
std_S2oS1_n=empty_like(S1_bins)
E_S1_n=empty_like(S1_bins)
coeff_n=[]
var_matrix_n=[]
#Find the NR S2/S1 band at each S1
det_cuts= (S1c>0) & (S2c>0) & (S2>=S2raw_min) & (Er>=Ermin)
for index, S1s in enumerate(S1_bins):
cut=det_cuts & inrange(S1c,[S1s-1/2,S1s+1/2])
S1_bin_cen_n[index]=mean(S1c[cut])
mean_S2oS1_n[index]=mean(log10(S2c[cut]/S1c[cut]))
std_S2oS1_n[index]=std(log10(S2c[cut]/S1c[cut]))
E_S1_n[index]=mean(Er[cut]) #average E for a given S1
#fit Gauss to distibution
# p0 is the initial guess for the fitting coefficients (A, mu and sigma above)
#hist, bin_edges = np.histogram(log10(S2c[cut]/S1c[cut]), 20, density=True)
#bin_centres = (bin_edges[:-1] + bin_edges[1:])/2
#p0 = [max(hist), mean_S2oS1_n[index], std_S2oS1_n[index]]
#coeff, var_matrix = curve_fit(gauss, bin_centres, hist, p0=p0)
#coeff_n.append(coeff)
#var_matrix_n.append(var_matrix)
#convert to numpy array. 3xN matrix
#coeff_n=np.array(coeff_n) #indexed as: amp, mean, sigma
#var_matrix_n=np.array(var_matrix_n)
#get NR mean, with a smooth spline(need this for discrimination calculation)
if nSim>=5e5:
sNR = ip.UnivariateSpline(S1_bin_cen_n, mean_S2oS1_n,s=.0001) #essentially linear interp
else:
sNR = ip.UnivariateSpline(S1_bin_cen_n, mean_S2oS1_n,s=.01) #essentially linear interp
#Use gaussian fit
#sNR = ip.UnivariateSpline(S1_bin_cen_n, coeff_n[:,1],s=0.005)
#Calc Nr Efficiency vs E
E_bins=linspace(0,maxEr,maxEr)
E_bin_cen_n=empty_like(E_bins)
Eff_n=empty_like(E_bins)
det_cuts= (S1c>0) & (S2>=S2raw_min)
for index, Es in enumerate(E_bins):
cut=det_cuts & inrange(Er,[Es-0.5,Es+0.5])
E_bin_cen_n[index]=mean(Er[cut])
Eff_n[index]=sum(cut)/sum(inrange(Er,[Es-0.5,Es+0.5])) #cut/total_in_bin
#Calculate the ER band and discrimination ####################################################
maxEe=30 #keVee, for flat ER spectrum
Flat_Ee = maxEe*st.uniform.rvs(size=nSim); #0-50 keVee
## Generate Signal in the detector ##
Nph=[]
Ne=[]
S1=[]
S2=[]
S1c=[]
S2c=[]
NEST.SetParticleType(1) #set to ER events
for en in Flat_Ee:
NEST.SetEnergy(en)
NEST.DetectorResponse()
Nph.append(NEST.GetNumPhotons())
Ne.append(NEST.GetNumElectrons())
S1.append(NEST.GetS1())
S2.append(NEST.GetS2())
S1c.append(NEST.GetS1c())
S2c.append(NEST.GetS2c())
Nph=np.array(Nph)
Ne=np.array(Ne)
S1=np.array(S1)
S2=np.array(S2)
S1c=np.array(S1c)
S2c=np.array(S2c)
S1_bins=linspace(1,maxS1,maxS1)
S1_bin_cen_e=empty_like(S1_bins)
mean_S2oS1_e=empty_like(S1_bins)
std_S2oS1_e=empty_like(S1_bins)
num_leak_e=empty_like(S1_bins)
num_total_e=empty_like(S1_bins)
leak_gauss_e=empty_like(S1_bins)
E_S1_e=empty_like(S1_bins)
#Find the ER S2/S1 band at each S1
det_cuts= (S1c>0) & (S2c>0) & (S2>=S2raw_min)
for index, S1s in enumerate(S1_bins):
cut=det_cuts & inrange(S1c,[S1s-1/2,S1s+1/2])
S1_bin_cen_e[index]=mean(S1c[cut])
mean_S2oS1_e[index]=mean(log10(S2c[cut]/S1c[cut]))
std_S2oS1_e[index]=std(log10(S2c[cut]/S1c[cut]))
#calculate discrimination
num_leak_e[index]=sum(log10(S2c[cut]/S1c[cut])<sNR(S1c[cut])) #num below NR mean
num_total_e[index]=sum(cut)
#Gaussian overlap
nsig=(mean_S2oS1_e[index]-sNR(S1_bin_cen_e[index]))/std_S2oS1_e[index] #(meanER-meanNR)/sigER
leak_gauss_e[index]=sp.special.erfc(nsig/sqrt(2))/2
E_S1_e[index]=mean(Flat_Ee[cut]) #average E for a given S1
#Calc Er Efficiency vs E
E_bins=linspace(0,maxEe,maxEe)
E_bin_cen_e=empty_like(E_bins)
Eff_e=empty_like(E_bins)
det_cuts= (S1c>0) & (S2>=S2raw_min)
for index, Es in enumerate(E_bins):
cut=det_cuts & inrange(Flat_Ee,[Es-0.5,Es+0.5])
E_bin_cen_e[index]=mean(Flat_Ee[cut])
Eff_e[index]=sum(cut)/sum(inrange(Flat_Ee,[Es-0.5,Es+0.5])) #cut/total_in_bin
return S1_bin_cen_n, mean_S2oS1_n, std_S2oS1_n, S1_bin_cen_e, mean_S2oS1_e, std_S2oS1_e, E_bin_cen_e, Eff_e, E_S1_e, E_bin_cen_n, Eff_n, E_S1_n, num_leak_e, num_total_e, leak_gauss_e, sNR
