# TTTT Analysis¶

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

In [1]:

    import ROOT
    from IPython.display import display, display_markdown
    %load_ext autoreload
    %autoreload 2
    import random
    from utils import ResultSet
    from utils import clear, show_event, show_value, show_function, normalize_columns, CANVAS, PDG

    Welcome to JupyROOT 6.08/02

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 [2]:

    rs_TTZ = ResultSet("TTZ", "../data/TTZToLLNuNu_treeProducerSusyMultilepton_tree.root")
    rs_TTW = ResultSet("TTW", "../data/TTWToLNu_treeProducerSusyMultilepton_tree.root")
    rs_TTTT = ResultSet("TTTT", "../data/TTTT_ext_treeProducerSusyMultilepton_tree.root")

    Running analysis for sample:  TTZ
    Writing log data to ../data/TTZToLLNuNu_treeProducerSusyMultilepton_tree_result.log
    Running analysis for sample:  TTW
    Writing log data to ../data/TTWToLNu_treeProducerSusyMultilepton_tree_result.log
    Running analysis for sample:  TTTT
    Writing log data to ../data/TTTT_ext_treeProducerSusyMultilepton_tree_result.log

In [3]:

    def plot_Jet_eta_phi(dataset, event_number = None):
        import matplotlib.pyplot as plt
        %matplotlib inline
        if event_number is None:
            event_number = random.randint(0,len(dataset.Jet_pt)-1)
        phis = list(dataset.Jet_phi[event_number])
        etas = list(dataset.Jet_eta[event_number])
        mc_phis = list(dataset.GenPart_phi[event_number])
        mc_etas = list(dataset.GenPart_eta[event_number])
        mc_ids = list(dataset.GenPart_pdgId[event_number])

        mc_phis,mc_etas = zip(*[(phi, eta) for phi, eta, pdgid in zip(mc_phis,mc_etas,mc_ids) if abs(pdgid) in {1,2,3,4,5,21}])

        plt.plot(phis,etas, 'r.', label='Jets')
        plt.plot(mc_phis,mc_etas, 'b.', label='GenPart')
        plt.xlim(-3.14159, 3.14159)
        plt.ylim(-5,5)
        plt.xlabel('Phi')
        plt.ylabel('Eta')
        plt.title("Jet/GenPart distribution for event {}".format(event_number))
        plt.grid()
        plt.legend()
        plt.show()

    plot_Jet_eta_phi(rs_TTZ)

In [4]:

    img, fns = show_value(rs_TTTT, 'reco_top_mass')
    display(img)
    display(fns)
    # display(show_value(hists_TTTT.mc_top_mass))

## t_mass¶

    ([](const std::tuple<Jet, Jet> &w, const Jet &b) {
      return (std::get<0>(w).v + std::get<1>(w).v + b.v).M();
    })

## dup_filter¶

    ([](const std::tuple<Jet, Jet> &w, const Jet &b) {
      int j0 = b.idx;
      int j1 = std::get<0>(w).idx;
      int j2 = std::get<1>(w).idx;
      return (j0 != j1) && (j0 != j2) && (j1 != j2);
    })

## w_mass_filter¶

    ([ win_l = W_MASS - 10, win_h = W_MASS + 10 ](const Jet &j1, const Jet &j2) {
      float inv_mass = (j1.v + j2.v).M();
      return inv_mass > win_l && inv_mass < win_h;
    })

## build_reco_jets¶

    ([](const std::vector<TLorentzVector> &vs, const std::vector<float> &b_cmvas) {
      std::vector<Jet> jets;
      for (int i = 0; i < vs.size(); i++) {
        jets.push_back(Jet::reco(vs[i], i, b_cmvas[i]));
      }
      return jets;
    })

## b_mva_filter¶

    ([cut = 0.0](const Jet &j) { return j.b_cmva > cut; })

## build_reco_jets¶

    ([](const std::vector<TLorentzVector> &vs, const std::vector<float> &b_cmvas) {
      std::vector<Jet> jets;
      for (int i = 0; i < vs.size(); i++) {
        jets.push_back(Jet::reco(vs[i], i, b_cmvas[i]));
      }
      return jets;
    })

In [6]:

    n = 0
    def to_str(vec):
        return '|'.join(map(lambda s: "{!s:<4s}".format(PDG.get(s,s)),vec))
    print(to_str(rs_TTTT.Jet_mcFlavour[n]))
    print(to_str(rs_TTTT.Jet_mcMatchFlav[n]))
    print(to_str(rs_TTTT.Jet_mcMatchId[n]))
    show_event(rs_TTTT, n)

Out[6]:

    s   |b̄  |b̄  |g   |b   |g   |c   |s̄  |g
    0   |b   |b   |0   |b   |0   |s   |s   |0
    0   |t̄  |t̄  |0   |t   |0   |t̄  |t   |0

In [7]:

    clear()
    ResultSet.hist_array_single('dijet_inv_mass')
    CANVAS.Draw()

In [8]:

    clear()
    ResultSet.hist_array_single('reco_top_mass')
    CANVAS.Draw()

In [9]:

    clear()
    ResultSet.hist_array_single('mc_top_mass')
    CANVAS.Draw()

In [11]:

    clear()
    ResultSet.hist_array_single('dijet_inv_mass_ssdilepton')
    CANVAS.Draw()

In [12]:

    clear()
    ResultSet.hist_array_single('dijet_inv_mass_osdilepton')
    CANVAS.Draw()

In [13]:

    clear()
    ResultSet.hist_array_single('dijet_inv_mass_trilepton')
    CANVAS.Draw()

In [14]:

    CANVAS.Clear()
    ResultSet.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 [15]:

    CANVAS.Clear()
    ResultSet.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 [16]:

    rs_TTTT.draw()

In [19]:

    rs_TTTT.nLepvsnJet_norm = normalize_columns(rs_TTTT.nLepvsnJet)
    rs_TTZ.nLepvsnJet_norm = normalize_columns(rs_TTZ.nLepvsnJet)
    rs_TTW.nLepvsnJet_norm = normalize_columns(rs_TTW.nLepvsnJet)
    clear()
    rs_TTZ.nLepvsnJet_norm.Draw('COLZ')
    CANVAS.Draw()

In [20]:

    rs_TTTT.nLepvsnJet_norm = normalize_columns(rs_TTTT.genEle_count_v_recEle_count)
    rs_TTZ.nLepvsnJet_norm = normalize_columns(rs_TTZ.genEle_count_v_recEle_count)
    rs_TTW.nLepvsnJet_norm = normalize_columns(rs_TTW.genEle_count_v_recEle_count)
    clear()
    rs_TTTT.nLepvsnJet_norm.Draw('COLZ')
    CANVAS.Draw()

In [22]:

    event_number = int(random.uniform(0,100))
    TTZ_event = show_event(rs_TTZ, event_number)
    TTW_event = show_event(rs_TTW, event_number)
    TTTT_event = show_event(rs_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 #87 in each dataset.

- TTZ Generator-Level Particles

- TTW Generator-Level Particles

- TTTT Generator-Level Particles

In [23]:

    ResultSet.stack_hist("lepton_count", title="Lepton Multiplicity",
                         enable_fill=True, normalize_to=1, make_legend=True, draw=True)

Out[23]:

    <ROOT.THStack object ("lepton_count_stack") at 0x18a159c0>

In [24]:

    ResultSet.stack_hist("b_jet_count", title="B-Jet Multiplicity",
                         enable_fill=True, normalize_to=1, make_legend=True, draw=True)

Out[24]:

    <ROOT.THStack object ("b_jet_count_stack") at 0x17f6b240>

In [26]:

    display(show_value(rs_TTTT, rs_TTTT.dijet_inv_mass_osdilepton)[0])
    display(show_value(rs_TTTT, "GenPart_pdgId_counter")[0])

In [ ]: