ROOT.gROOT.SetBatch()
exposure_time_in_days = 5
time_in_days = 10 * 365.25
kg = 30
upper_energy = 20
lower_energy = 0.5
axion_mass = 0.3
basevars = base_model.BaseVariables(5, time_in_days / 365.25, lower_energy,
upper_energy)
time = basevars.get_time()
energy = basevars.get_energy()
energy.setBins(64)
time.setBins(64)
tritium_decay = TritiumDecayModel(basevars, exposure_time_in_days, 200, kg,
0.001)
total_background = tritium_decay.get_model()
total_background.getVariables().writeToStream(ROOT.cout, False)
data = total_background.generate(ROOT.RooArgSet(energy, time))
total_background.fitTo(data)
c1 = ROOT.TCanvas()
for var in [energy, time]:
frame = var.frame()
data.plotOn(frame)
total_background.plotOn(frame)
total_background.plotOn(frame, ROOT.RooFit.Components("*flat_extend*"),
def initialize(self):
if self.is_initialized: return
from pyWIMP.DMModels.wimp_model import WIMPModel
from pyWIMP.DMModels.base_model import BaseVariables
from pyWIMP.DMModels.flat_model import FlatModel
self.total_counts = int(self.mass_of_detector*
self.background_rate*
(self.energy_max-self.threshold)*
self.total_time*365)
self.basevars = BaseVariables(time_beginning=0,
time_in_years=self.total_time,
energy_threshold=self.threshold,
energy_max=self.energy_max)
if self.num_energy_bins != 0: self.basevars.get_energy().setBins(int(self.num_energy_bins))
if self.num_time_bins != 0: self.basevars.get_time().setBins(int(self.num_time_bins))
self.variables = ROOT.RooArgSet()
if self.constant_time:
self.basevars.get_time().setVal(0)
self.basevars.get_time().setConstant(True)
else:
self.variables.add(self.basevars.get_time())
if not self.constant_energy:
self.variables.add(self.basevars.get_energy())
self.calculation_class = \
ec.ExclusionCalculation(self.exit_manager)
# This is where we define our models
self.backgroundClass = TritiumDecayModel(self.basevars,
self.tritium_exposure_time,
self.tritium_activation_rate,
self.mass_of_detector,
self.background_rate)
# This model is already an extended model
self.background_model = self.backgroundClass.get_model()
self.background_extend = self.background_model
if not self.do_axioelectric:
# The following has not been normalized to per-nucleon yet.
self.model_normal = ROOT.RooRealVar("model_normal",
"WIMP-nucleus #sigma",
0, -1e-15, 0.1, 'pb')
self.wimpClass = WIMPModel(self.basevars,
mass_of_wimp=self.wimp_mass,
kilograms = self.mass_of_detector,
constant_quenching=(not self.variable_quenching))
self.model = self.wimpClass.get_model()
self.norm = self.wimpClass.get_normalization().getVal()
self.model_extend = ROOT.RooExtendPdf("model_extend",
"model_extend",
self.model,
self.model_normal)
else:
# wimpClass is of course a misnomer here, but we use it for now
self.wimpClass = GaussianSignalModel(self.basevars,
mean_of_signal=self.axion_mass)
self.model_normal = ROOT.RooRealVar("model_normal",
"Counts",
0, -10, 1000)
# This is where we define our model
self.model = self.wimpClass.get_model()
self.norm = self.wimpClass.get_normalization()*self.model.getNorm(
ROOT.RooArgSet(self.basevars.get_energy()))
# We actually want the inverse of the normaliation, since this is later multiplied
# and we want the total counts in a particular gaussian.
self.norm = 1./self.norm
self.model_extend = ROOT.RooExtendPdf("model_extend",
"model_extend",
self.model,
self.model_normal)
self.added_pdf = ROOT.RooAddPdf("b+s",
"Background + Signal",
ROOT.RooArgList(
self.background_extend,
self.model_extend))
self.test_variable = self.model_normal
self.data_set_model = self.background_extend
self.fitting_model = self.added_pdf
self.is_initialized = True
