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'
plt.plot(MT, MBB, color, linewidth=2.5, ls=linestyle)
ax = plt.subplot(111)
plt.axis(XA[i])
plt.xlabel('Exposure (kg year)')
plt.ylabel(r'$m_{\beta\beta}$ (meV)')
### This script generates a table with the constant A for all isotopes
### for a given NME calculation method.
### N.B. The script expects to be living within the folder 'examples'
### of the pybbsens distribution.
import os.path
FILE_PATH = os.path.dirname(os.path.realpath(__file__))
import sys
sys.path.append(FILE_PATH + '/..')
from pybbsens import isotope
from pybbsens import nmeset
from pybbsens import units
import math
isodb = isotope.isotopes
isotope.SelectNMESet(nmeset.nmedb['ISM']) #<< Choose here your favourite method
print "Isotope\t A [meV (kg yr)^-1/2]"
print "--------------------------------"
for symbol in isodb:
A = isodb[symbol].constant_A() / (units.meV * math.sqrt(units.kg*units.year))
print "%s\t %.0f" % (symbol, A)
import os.path FILE_PATH = os.path.dirname(os.path.realpath(__file__)) DATA_PATH = FILE_PATH + '/../data/' import sys sys.path.append(FILE_PATH + '/..') import csv from numpy import arange from pybbsens import units from pybbsens import isotope from pybbsens import experiment from pybbsens import conflimits from pybbsens import nmeset isotope.SelectNMESet(nmeset.nmedb['QRPA_Jy']) ######################################## ### EXPERIMENT DATA #################### ### The following parameters are not relevant for this calculation. ### 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.
DATA_PATH = FILE_PATH + '/../data/'
import sys
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 = "EXO200"
isot = isotope.Xe136
eff = 0.846
eres = isot.Qbb * 0.0153 * 2.35
bkg = 5.E-3 / (units.keV * units.kg * units.year)
mass = 76. * units.kg
expo = 100. * units.kg * units.year
EXO200 = experiment.Experiment(name, isotope.Xe136, eff, eres, bkg, mass)
FCM = conflimits.FCMemoizer(0.9)
FCM.ReadTableAverageUpperLimits(DATA_PATH + 'FC90.dat')
for key in nmeset.nmedb:
print "NME = ", key
isotope.SelectNMESet(nmeset.nmedb[key])
mbb = EXO200.sensitivity(expo, FCM)
print "mbb (meV) =", mbb / units.meV
hl = isot.half_life(mbb)
print "Tonu (year) =", hl / units.year
