st.setBins(200, 'cache')
t = RealVar('time', Title = 'decay time', Unit='ps', Observable = True, MinMax = (-1, 1))
from P2VV.Parameterizations.SigmatPDFs import DoubleLogNormal
dln = DoubleLogNormal(st, frac_ln2 = dict(Value = 0.312508), k1 = dict(Value = 0.801757),
k2 = dict(Value = 1.37584), median = dict(Value = 0.0309409))
ln = dln.pdf()
# Resolution models
from P2VV.Parameterizations.TimeResolution import Gaussian_TimeResolution as TimeResolution
from P2VV.Parameterizations.TimeResolution import Multi_Gauss_TimeResolution as Multi_TimeResolution
tres_args = dict(time = t, sigmat = st, Cache = True)
tres_1 = Multi_TimeResolution(Name = 'tres', ParNamePrefix = 'one',
TimeResSFParam = 'linear',
timeResMu = dict(Value = 0.),
sf_one_offset = dict(Value = 0, Constant = True),
sf_one_slope = dict(Value = 1.174),
sf_two_offset = dict(Value = 0, Constant = True),
sf_two_slope = dict(Value = 2),
Fractions = [(2, 0.143)], **tres_args)
tres_2 = TimeResolution(Name = 'tres', ParNamePrefix = 'two', PerEventError = True,
timeResSigmaSF = dict(Value = 4.), **tres_args)
# Gaussians for Time
from P2VV.Parameterizations.TimePDFs import Prompt_Peak
g1 = Prompt_Peak(t, tres_1.model(), Name = 'g1')
g2 = Prompt_Peak(t, tres_2.model(), Name = 'g2')
# Mass shapes
from P2VV.Parameterizations.MassPDFs import Background_PsiMass as PsiBkgPdf
bkg_m = PsiBkgPdf(mpsi, Name = 'bkg_mpsi')
from ROOT import RooExponential as Exponential
from ROOT import RooDecay as Decay
# Time resolution model
sig_tres = None
mu = dict(MinMax = (-0.010, 0.010))
mu['Constant'] = False
mu['Value'] = -0.00407301
from P2VV.Parameterizations.TimeResolution import Multi_Gauss_TimeResolution as TimeResolution
## Signal and background time resolutions.
tres_args = dict(time = time_obs, sigmat = st, Cache = True,
Parameterise = 'RMS', TimeResSFParam = 'linear_no_offset', timeResMu = mu,
ScaleFactors = [(2, 2.1), (1, 1.26)], Fractions = [(2, 0.2)])
sig_tres = TimeResolution(Name = 'sig_tres', **tres_args)
# Resolution models
bkg_tres = TimeResolution(Name = 'bkg_tres', ParNamePrefix = 'bkg', time = time_obs, sigmat = st,
Cache = True, Parameterise = 'RMS', TimeResSFParam = 'linear_no_offset',
timeResMu = mu, ScaleFactors = [(2, 2.1), (1, 1.26)], Fractions = [(2, 0.2)])
# J/psi mass pdf
from P2VV.Parameterizations.MassPDFs import DoubleCB_Psi_Mass as PsiMassPdf
psi_m = PsiMassPdf(mpsi, Name = 'psi_m', mpsi_alpha_1 = dict(Name = 'mpsi_alpha_1', Value = 2., Constant = True))
psi_m = psi_m.pdf()
# J/psi background
from P2VV.Parameterizations.MassPDFs import Background_PsiMass as PsiBkgPdf
bkg_mpsi = PsiBkgPdf(mpsi, Name = 'bkg_mpsi')
project_vars = [st, excl_biased]
# now build the actual signal PDF...
from ROOT import RooGaussian as Gaussian
from ROOT import RooExponential as Exponential
from ROOT import RooDecay as Decay
# B time PDF
from P2VV.Parameterizations.TimeResolution import Multi_Gauss_TimeResolution as TimeResolution
tres_args = dict(time = t, sigmat = st, Cache = True,
Parameterise = 'RMS',
TimeResSFParam = 'linear', SplitFracs = False,
ScaleFactors = [(2, 2.1), (1, 1.26)],
Fractions = [(2, 0.2)], SplitMean = False)
tres = TimeResolution(Name = 'tres', **tres_args)
from P2VV.Parameterizations.TimePDFs import Single_Exponent_Time as TimePdf
sig_t = TimePdf(t, tres.model())
# B mass pdf
from P2VV.Parameterizations.MassPDFs import DoubleGauss_Signal_Mass as MassPdf
sig_m = MassPdf(m, Name = 'sig_m', AvSigParameterisation = True)
sig_m = sig_m.pdf()
# background
psi_c = RealVar( 'psi_c', Unit = '1/MeV', Value = -0.0004, MinMax = (-0.1, -0.0000001))
bkg_m = Pdf(Name = 'bkg_m', Type = Exponential, Parameters = [m, psi_c])
background = Component('background', (bkg_m,), Yield= (200000,500,5000000) )
signal = Component('signal', (sig_m, sig_t.pdf()), Yield= (200000,500,500000) )
