def chi2_2t4ebins_newpull(n, ac, meas, epsee, epsmm, epsem, epset, epsmt,
alpha, beta, gamma):
chi = 0
sig = signalcomp(epsee, epsmm, epsem, epset, epsmt)
sig_n = add_lists_2(sig, n, alpha, beta)
smeared = acc.smearing_fct(sig_n)
pred = acc.data_eff(smeared)
#add bkg
lista = add_lists_1(pred, ac, gamma)
obss = switch_te(meas)
#rebin
preddev = rebin_list_4E2t(lista)
obssev = rebin_list_4E2t(obss)
for i in range(len(preddev)):
if preddev[i, 2] >= 0:
numevents = preddev[i, 2]
else:
numevents = 10000 #in case the minimizer leads to a negative number of events
numobs = obssev[i, 2]
if numobs == 0:
add = numevents - numobs
else:
add = numevents - numobs + numobs * np.log(numobs / numevents)
chi += add
if (alpha + 1.) <= 0 or (beta + 1.) <= 0 or (gamma + 1.) <= 0:
return np.abs(alpha * 1000.0) + np.abs(beta * 1000.0) + np.abs(
gamma * 1000.0) + 2 * chi
else:
return 2 * chi + 2 * (alpha - np.log(alpha + 1.)) / 0.28**2 + 2 * (
beta - np.log(beta + 1.)) / 0.25**2 + 2 * (
gamma - np.log(gamma + 1.)) / 0.0855**2
def chi2_144bins_newpull(n, ac, meas, epsee, epsmm, epsem, epset, epsmt, alpha,
beta, gamma):
chi = 0
#add signal and neutron bkg
sig = signalcomp(epsee, epsmm, epsem, epset, epsmt)
sig_n = add_lists_2(sig, n, alpha, beta)
smeared = acc.smearing_fct(sig_n)
pred = acc.data_eff(smeared)
#add bkg
lista = add_lists_1(pred, ac, gamma)
for i in range(len(pred)):
if lista[i, 2] >= 0:
numevents = lista[i, 2]
else:
numevents = 10000 #in case the minimizer leads to a negative number of events
numobs = meas[i, 2]
if numobs == 0:
add = numevents - numobs
else:
add = numevents - numobs + numobs * np.log(numobs / numevents)
chi += add
if (alpha + 1.) <= 0 or (beta + 1.) <= 0 or (gamma +
1.) <= 0: #avoid log(0)
return np.abs(alpha * 1000.0) + np.abs(beta * 1000.0) + np.abs(
gamma * 1000.0) + 2 * chi
else:
return 2 * chi + 2 * (alpha - np.log(alpha + 1.)) / 0.28**2 + 2 * (
beta - np.log(beta + 1.)) / 0.25**2 + 2 * (
gamma - np.log(gamma + 1.)) / 0.171**2
def chi2_bins(n,ac,meas,epsee,epsmm,epsem,epset,epsmt,alpha,beta,gamma):
chi=0
sig=csir.signalcomp(epsee,epsmm,epsem,epset,epsmt) #calculate signal for given NSI parameters
sig_n=csir.add_lists_2(sig,n,alpha,beta) #add signal and neutron background
smeared=acc.smearing_fct(sig_n) #apply smearing and efficiency
pred=acc.data_eff(smeared)
#add bkg
lista=csir.add_lists_1(pred,ac,gamma)
#that's the prediction
preddev=lista
for i in range(len(preddev)):
if preddev[i,2]>=0:
numevents = preddev[i, 2]
else:
numevents=10000 #in case the minimizer goes to a negative number of events
numobs = meas[i, 2] #that's the data
if numobs == 0: #if data is zero
add = numevents - numobs
else:
add = numevents - numobs + numobs*np.log(numobs/numevents)
chi += add
# return 2*chi+(alpha/0.28)**2+(beta/0.25)**2+(gamma/0.171)**2 #uncomment if Gaussian pull terms are used
if (alpha + 1.)<=0 or (beta + 1.)<=0 or (gamma + 1.)<=0: #if asymmetric pull terms are used prevent that the log(0) case
return np.abs(alpha*1000.0)+np.abs(beta*1000.0)+np.abs(gamma*1000.0)+2*chi
else:
return 2*chi+ 2*(alpha - np.log(alpha + 1.))/0.28**2+2*(beta - np.log(beta + 1.))/0.25**2+2*(gamma - np.log(gamma + 1.))/0.171**2
def randompred(sig,alphabf,betabf,gammabf,n,bkg):
sig_n=csir.add_lists_2(sig,n,alphabf,betabf) #adds signal and neutron background
smeared=acc.smearing_fct(sig_n) #applies smearing
pred=acc.data_eff(smeared) #applies efficiency
#add bkg
lista=csir.add_lists_1(pred,bkg,gammabf)
return lista
def randompred(sig, n, bkg):
alpha = np.random.normal(0, 0.28)
beta = np.random.normal(0, 0.25)
sig_n = csir.add_lists_2(sig, n, alpha, beta)
smeared = acc.smearing_fct(sig_n)
pred = acc.data_eff(sig_n)
#add bkg
lista = csir.add_lists_1(pred, bkg, 0)
return lista
def chi2_bins(n, ac, meas, alpha, beta, gamma):
chi = 0
sig = csir.signalcomp(0.0, 0.0, 0.0, 0.0, 0.0)
sig_n = csir.add_lists_2(sig, n, alpha, beta)
smeared = acc.smearing_fct(sig_n)
pred = acc.data_eff(smeared)
#add bkg
lista = csir.add_lists_1(pred, ac, gamma)
preddev = lista
for i in range(len(preddev)):
numevents = preddev[i, 2]
numobs = meas[i, 2]
if numobs == 0:
add = numevents - numobs
else:
add = numevents - numobs + numobs * np.log(numobs / numevents)
chi += add
return 2 * chi + (alpha / 0.28)**2 + (beta / 0.25)**2 + (gamma / 0.171)**2