TTTT Analysis

This Notebook is simply a playground to examine the resulting histograms from the main TTTT analysis executable.

In [102]:
import ROOT
from IPython.display import display, display_markdown
%load_ext autoreload
%autoreload 2
import random
from utils import HistCollection as HC
from utils import clear, show_event, normalize_columns, CANVAS, PDG

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

First, we need to load the pre-processed datafiles. These will generally contain a set of histograms of various quantities calculated from data in the input MiniTrees. However, they can also contain things besides histograms. For example, C++ STL containers can be serialized to the ROOT file to save things such as counters or even "raw" event information.

In [103]:
hists_TTZ = HC("TTZ", "../data/TTZToLLNuNu_treeProducerSusyMultilepton_tree.root")
hists_TTW = HC("TTW", "../data/TTWToLNu_treeProducerSusyMultilepton_tree.root")
hists_TTTT = HC("TTTT", "../data/TTTT_ext_treeProducerSusyMultilepton_tree.root")

Loading unchanged result file  ../data/TTZToLLNuNu_treeProducerSusyMultilepton_tree_result.root
Loading unchanged result file  ../data/TTWToLNu_treeProducerSusyMultilepton_tree_result.root
Loading unchanged result file  ../data/TTTT_ext_treeProducerSusyMultilepton_tree_result.root
In [104]:
clear()
HC.hist_array_single('Jet_energy_vs_Jet_eta')
CANVAS.Draw()
In [105]:
clear()
HC.hist_array_single('dijet_inv_mass')
CANVAS.Draw()
In [106]:
clear()
HC.hist_array_single('dijet_inv_mass_ssdilepton')
CANVAS.Draw()
In [115]:
clear()
HC.hist_array_single('dijet_inv_mass_osdilepton')
CANVAS.Draw()
In [116]:
clear()
HC.hist_array_single('dijet_inv_mass_trilepton')
CANVAS.Draw()
In [107]:
CANVAS.Clear()
HC.stack_hist_array(*zip(('jet_count_os_dilepton','Jet Multiplicity for Opposite-Sign Dilepton Events'),
                         ('jet_count_ss_dilepton','Jet Multiplicity for Same-Sign Dilepton Events'),
                         ('jet_count_trilepton', 'Jet Multiplicity for Trilepton Events')
                        ),
                    normalize_to=0,
                    enable_fill=True,
                    shape=(3,1),
                   )
CANVAS.Draw()
In [108]:
CANVAS.Clear()
HC.stack_hist_array(*zip(('jet_count_os_dilepton','Jet Multiplicity for Opposite-Sign Dilepton Events'),
                         ('jet_count_ss_dilepton','Jet Multiplicity for Same-Sign Dilepton Events'),
                         ('jet_count_trilepton', 'Jet Multiplicity for Trilepton Events')
                        ),
                    normalize_to=0,
                    enable_fill=True,
                    shape=(3,1),
                    draw_option='nostack',
                   )
CANVAS.Draw()
In [109]:
hists_TTTT.draw()
In [110]:
hists_TTTT.nLepvsnJet_norm = normalize_columns(hists_TTTT.nLepvsnJet)
hists_TTZ.nLepvsnJet_norm = normalize_columns(hists_TTZ.nLepvsnJet)
hists_TTW.nLepvsnJet_norm = normalize_columns(hists_TTW.nLepvsnJet)
clear()
hists_TTTT.nLepvsnJet_norm.Draw('COLZ')
CANVAS.Draw()
In [111]:
event_number = int(random.uniform(0,100))
TTZ_event = show_event(hists_TTZ, event_number)
TTW_event = show_event(hists_TTW, event_number)
TTTT_event = show_event(hists_TTTT, event_number)

We can use the show_event function to look at the Generator-Level particles for the event. They are color-coded based on their pt relative to the maximum pt of a particles in the event. Darker is lower, greener/lighter is higher. The following are the particle trees for event #10 in each dataset.

  • TTZ Generator-Level Particles
  • TTW Generator-Level Particles
  • TTTT Generator-Level Particles
In [112]:
HC.stack_hist("lepton_count", title="Lepton Multiplicity",
              enable_fill=True, normalize_to=1, make_legend=True, draw=True)
Out[112]:
<ROOT.THStack object ("lepton_count_stack") at 0x4fc43720>
In [113]:
HC.stack_hist("b_jet_count", title="B-Jet Multiplicity",
              enable_fill=True, normalize_to=1, make_legend=True, draw=True)
Out[113]:
<ROOT.THStack object ("b_jet_count_stack") at 0x20de0100>
In [114]:
from functools import reduce
from collections import defaultdict
# all_eq = lambda l: reduce(lambda ll, y: (ll[0] and ll[1]==y, y), l, (True, l[0]))
all_eq = lambda l: (len(set(l)) == 1, set(l))

def counter(l):
    cntr = defaultdict(int)
    for i in l:
        cntr[i] += 1
    # return cntr
    for key in sorted(cntr.keys()):
        print("{: 7d}: {: 7d}".format(key, cntr[key]))
ss = sorted(hists_TTTT.evt)
len(ss) / (ss[-1] - ss[0])
# print(counter(hists_TTTT.lumi))
# l = list(hists_TTTT.lumi)
# print(sorted(l) == l)
# for l1, l2 in zip(l, sorted(l)):
#     print("{:8s} => {: 7d}, {: 7d}".format(str(l1 == l2), l1, l2))
# print(all_eq(hists_TTTT.lumi))
# print(all_eq(hists_TTZ.xsec))
# print(all_eq(hists_TTW.xsec))
Out[114]:
0.013414741998788721