def BackgroundFit_f4_trigcorr_bbh(x, par): if (1 + par[1]*x[0]/8.e3 + par[2] * (x[0]/8.e3)**2) <= 0.: return 0 elif ((1 + par[1]*x[0]/8.e3 + par[2] * (x[0]/8.e3)**2)**par[3]) < 1.e-15: return 0 else: return par[0] / ((1 + par[1]*x[0]/8.e3 + par[2] * (x[0]/8.e3)**2)**par[3]) * trigger_efficiency.trigger_efficiency_bbh(x[0])
def BackgroundFit_f6_trigcorr_bbh(x, par):
if 1 + par[1] * x[0] / 8.e3 + par[2] * (x[0] / 8.e3)**2 + par[3] * (
x[0] / 8.e3)**3 <= 0:
return 0
elif ((1 + par[1] * x[0] / 8.e3 + par[2] * (x[0] / 8.e3)**2 + par[3] *
(x[0] / 8.e3)**3)**par[4]) < 1.e-15:
return 0
else:
return (par[0] /
((1 + par[1] * x[0] / 8.e3 + par[2] *
(x[0] / 8.e3)**2 + par[3] * (x[0] / 8.e3)**3)**
par[4])) * trigger_efficiency.trigger_efficiency_bbh(x[0])
mc_hist.SetName("mjj_mc_" + signal_region + "_" + strategy)
mc_hist.SetDirectory(0)
if strategy == "datatrigger":
if signal_region == "lowmass":
mc_hist.Scale(trigger_efficiency.online_btag_eff["trigbbl_CSVTM"][0])
elif signal_region == "highmass":
mc_hist.Scale(trigger_efficiency.online_btag_eff["trigbbh_CSVTM"][0])
data_hist = data_hist_raw.Clone()
for bin in xrange(1, data_hist.GetNbinsX() + 1):
mjj = data_hist.GetXaxis().GetBinCenter(bin)
if signal_region == "lowmass":
data_hist.SetBinContent(bin, data_hist.GetBinContent(bin) / trigger_efficiency.trigger_efficiency_bbl(mjj))
data_hist.SetBinError(bin, data_hist.GetBinError(bin) / trigger_efficiency.trigger_efficiency_bbl(mjj))
elif signal_region == "highmass":
data_hist.SetBinContent(bin, data_hist.GetBinContent(bin) / trigger_efficiency.trigger_efficiency_bbh(mjj))
data_hist.SetBinError(bin, data_hist.GetBinError(bin) / trigger_efficiency.trigger_efficiency_bbh(mjj))
# Zero histograms below where the trigger correction is known
if signal_region == "lowmass":
if mjj < 176.:
data_hist.SetBinContent(bin, 0.)
data_hist.SetBinError(bin, 0.)
mc_hist.SetBinContent(bin, 0.)
mc_hist.SetBinError(bin, 0.)
elif signal_region == "highmass":
if mjj < 325.:
data_hist.SetBinContent(bin, 0.)
data_hist.SetBinError(bin, 0.)
mc_hist.SetBinContent(bin, 0.)
mc_hist.SetBinError(bin, 0.)
elif strategy == "mctrigger":
def BackgroundFit_f5_trigcorr_bbh(x, par):
return par[0] * (x[0] / 8.e3)**(-1. * par[1]) * (1. - (x[0] / 8.e3)**(
1. / 3.))**par[2] * trigger_efficiency.trigger_efficiency_bbh(x[0])
def BackgroundFit_f3_trigcorr_bbh(x, par):
return par[0] / (
1 + par[1] *
(x[0] / 8.e3))**par[2] * trigger_efficiency.trigger_efficiency_bbh(
x[0])
def BackgroundFit_f1_trigcorr_bbh(x, par):
return par[0] * (1. - (x[0] / 8.e3))**par[1] / (
(x[0] / 8.e3)**(par[2] + par[3] * TMath.Log(
(x[0] / 8.e3)))) * trigger_efficiency.trigger_efficiency_bbh(x[0])
for bin in xrange(1, data_hist.GetNbinsX() + 1):
mjj = data_hist.GetXaxis().GetBinCenter(bin)
if signal_region == "lowmass":
data_hist.SetBinContent(
bin,
data_hist.GetBinContent(bin) /
trigger_efficiency.trigger_efficiency_bbl(mjj))
data_hist.SetBinError(
bin,
data_hist.GetBinError(bin) /
trigger_efficiency.trigger_efficiency_bbl(mjj))
elif signal_region == "highmass":
data_hist.SetBinContent(
bin,
data_hist.GetBinContent(bin) /
trigger_efficiency.trigger_efficiency_bbh(mjj))
data_hist.SetBinError(
bin,
data_hist.GetBinError(bin) /
trigger_efficiency.trigger_efficiency_bbh(mjj))
# Zero histograms below where the trigger correction is known
if signal_region == "lowmass":
if mjj < 176.:
data_hist.SetBinContent(bin, 0.)
data_hist.SetBinError(bin, 0.)
mc_hist.SetBinContent(bin, 0.)
mc_hist.SetBinError(bin, 0.)
elif signal_region == "highmass":
if mjj < 325.:
data_hist.SetBinContent(bin, 0.)
data_hist.SetBinError(bin, 0.)
