eff = 0.65
eres = isot.Qbb * 0.003
bkg = 1.0E-3 / (units.keV * units.kg * units.year)
mass = 429. * units.kg
expo = 2200. * units.kg * units.year
nmes = ["ISM", "IBM2"]
cnmes = ["blue", "red"]
########################################
### Create a confidence-limit calculator.
### We use in this case the Feldman-Cousins memoizer.
fcm = conflimits.FCMemoizer(0.9)
fcm.ReadTableAverageUpperLimits(DATA_PATH + 'FC90.dat')
exp = experiment.Experiment(name, isotope.Se82, eff, eres, bkg, mass)
i = 0
XA = [[3e+2, 1e+4, 2, 200], [3e+2, 1e+4, 2, 80]]
for nme in nmes:
isotope.SelectNMESet(nmeset.nmedb[nme])
MBB = []
MT = arange(10, 10010, 10.)
MBB = [
exp.sensitivity(mt * units.kg * units.year, fcm) / units.meV
for mt in MT
]
color = cnmes[i]
linestyle = 'solid'
sys.path.append(FILE_PATH + '/..')
from pybbsens import units
from pybbsens import isotope
from pybbsens import experiment
from pybbsens import conflimits
from pybbsens import nmeset
name = "GERDAII"
isot = isotope.Ge76
eff = 0.62
eres = isot.Qbb * 0.0015
bkg = 1.0E-3 / (units.keV * units.kg * units.year)
mass = 50. * units.kg
expo = 200. * units.kg * units.year
GERDA = experiment.Experiment(name, isotope.Ge76, eff, eres, bkg, mass)
FCM = conflimits.FCMemoizer(0.9)
FCM.ReadTableAverageUpperLimits(DATA_PATH + 'FC90.dat')
nmes = ["ISM", "IBM2", "QRPA_Tu", "QRPA_Jy", "EDF"]
for nme in nmes:
print "NME = ", nme
isotope.SelectNMESet(nmeset.nmedb[nme])
mbb = GERDA.sensitivity(expo, FCM)
print "mbb (meV) =", mbb / units.meV
hl = isot.half_life(mbb)
print "Tonu (year) =", hl / units.year
eff = 0.55
eres = isot.Qbb * 0.06
bkg = 1.0E-4 / (units.keV * units.kg * units.year)
mass = 429. * units.kg
expo = 2200. * units.kg * units.year
nmes = ["ISM", "IBM2"]
cnmes = ["blue", "red"]
########################################
### Create a confidence-limit calculator.
### We use in this case the Feldman-Cousins memoizer.
fcm = conflimits.FCMemoizer(0.9)
fcm.ReadTableAverageUpperLimits(DATA_PATH + 'FC90.dat')
exp = experiment.Experiment(name, isotope.Xe136, eff, eres, bkg, mass)
i = 0
for nme in nmes:
isotope.SelectNMESet(nmeset.nmedb[nme])
MBB = []
MT = arange(10, 10010, 10.)
MBB = [
exp.sensitivity(mt * units.kg * units.year, fcm) / units.meV
for mt in MT
]
color = cnmes[i]
i += 1
linestyle = 'solid'
### Choose dummy values.
eff = .3
res = 1. * units.keV
mass = 0.
### Background counts in ROI per unit of exposure
bkg_in_ROI = [x / (units.kg * units.year) for x in [0.1, 0.01, 0.001, 0.]]
########################################
### Create a confidence-limit calculator.
### We use in this case the Feldman-Cousins memoizer.
fcm = conflimits.FCMemoizer(0.9)
fcm.ReadTableAverageUpperLimits(DATA_PATH + 'FC90.dat')
########################################
### Loop now over the different cases, calculating the sensitivity
for b in bkg_in_ROI:
filename = 'XeExp_' + str(b * (units.kg * units.year)) + '.txt'
writer = csv.writer(open(filename, 'w'))
exp = experiment.Experiment("XeExp", isotope.Xe136, eff, res, b / res,
mass)
for exposure in arange(10., 10010., 10.):
exposure = exposure * units.kg * units.year
mbb = exp.sensitivity(exposure, fcm) / units.meV
exposure = exposure / (units.kg * units.year)
writer.writerow([exposure, mbb])
sys.path.append(FILE_PATH + '/..')
from pybbsens import units
from pybbsens import isotope
from pybbsens import experiment
from pybbsens import conflimits
from pybbsens import nmeset
name = "CUORE"
isot = isotope.Te130
eff = 0.87
eres = isot.Qbb * 0.002
bkg = 4.0E-2 / (units.keV * units.kg * units.year)
mass = 206. * units.kg
expo = 1000. * units.kg * units.year
CUORE = experiment.Experiment(name, isotope.Te130, eff, eres, bkg, mass)
FCM = conflimits.FCMemoizer(0.9)
FCM.ReadTableAverageUpperLimits(DATA_PATH + 'FC90.dat')
nmes = ["ISM", "IBM2", "QRPA_Tu", "QRPA_Jy", "EDF"]
for nme in nmes:
print "NME = ", nme
isotope.SelectNMESet(nmeset.nmedb[nme])
mbb = CUORE.sensitivity(expo, FCM)
print "mbb (meV) =", mbb / units.meV
hl = isot.half_life(mbb)
print "Tonu (year) =", hl / units.year