TTTT Analysis¶
This Notebook is simply a playground to examine the resulting histograms from the main TTTT analysis executable.
In [1]:
import ROOT
%load_ext autoreload
%autoreload 2
from utils import HistCollection as HC
from utils import show_event
First, we need to load(and optionally rebuild) the histogram datafiles. These will generally contain a set of histograms of various quantities calculated from data in the input MiniTrees.
In [2]:
rebuild_hists=False
hists_TTTT = HC("TTTT", "../data/TTTT_ext_treeProducerSusyMultilepton_tree.root", rebuild_hists=rebuild_hists)
hists_TTZ = HC("TTZ", "../data/TTZToLLNuNu_treeProducerSusyMultilepton_tree.root", rebuild_hists=rebuild_hists)
hists_TTW = HC("TTW", "../data/TTWToLNu_treeProducerSusyMultilepton_tree.root", rebuild_hists=rebuild_hists)
In [3]:
HC.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=1,
enable_fill=True,
shape=(3,1),
)
HC.canvas.Draw()
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. Green means higher, red means lower
In [4]:
show_event(hists_TTZ, 3)
Out[4]:
In [5]:
HC.stack_hist("lepton_count", title="Lepton Multiplicity",
enable_fill=True, normalize_to=1, make_legend=True, draw=True)
Out[5]:
In [6]:
HC.stack_hist("b_jet_count", title="B-Jet Multiplicity",
enable_fill=True, normalize_to=1, make_legend=True, draw=True)
Out[6]:
The draw method of the HistCollection class simply creates a grid of plots showing all of the drawable objects(i.e. objects with a Draw method) contained in the input file.
In [7]:
hists_TTTT.draw()
In [8]:
hists_TTZ.draw()
In [9]:
hists_TTW.draw()
