<jupyter_start><jupyter_text>The main discriminating variables in the analysis are Jet and B-Jet MultiplicitiesBelow are shown the distributions for the TTTT as well as relevant BG processes. The "baseline" selection for the trilepton channel is:  - Exactly three leptons  - Three or more B-Jets  - Z-Mass Window veto: The invariant mass of all same-flavor opposite-sign lepton pairs must be outside the Z-Mass window (70,105)GeV<jupyter_code>fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(2,2, figsize=(26,20), tight_layout=False)
stack('jet_count', ax0, 'Jet Multiplicity', cls=StackHistWithSignificance, high_cut_significance=True)
stack('jet_count_trilepton', ax1, 'Jet Multiplicity ($N_{lep}=3$)', cls=StackHistWithSignificance, high_cut_significance=True)
stack('jet_count_b_jet3', ax2, 'Jet Multiplicity ($N_{Jet_B}\ge3$)', cls=StackHistWithSignificance, scale=1, high_cut_significance=True)
stack('jet_count_z_mass_veto', ax3, 'Jet Multiplicity (Z-mass Veto)', cls=StackHistWithSignificance, scale=1, high_cut_significance=True)
fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(2,2, figsize=(26,20), tight_layout=False)
stack('b_jet_count', ax0, 'B-Jet Multiplicity', cls=StackHistWithSignificance, high_cut_significance=True)
stack('b_jet_count_trilepton', ax1, 'B-Jet Multiplicity ($N_{lep}=3$)', cls=StackHistWithSignificance, high_cut_significance=True)
stack('b_jet_count_b_jet3', ax2, 'B-Jet Multiplicity ($N_{Jet_B}\ge3$)', cls=StackHistWithSignificance, scale=1, high_cut_significance=True)
stack('b_jet_count_z_mass_veto', ax3, 'B-Jet Multiplicity (Z-mass Veto)', cls=StackHistWithSignificance, scale=1, high_cut_significance=True)
fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(2,2, figsize=(26,20), tight_layout=False)
stack('jet_count', ax0, 'Jet Multiplicity', cls=StackHistWithSignificance, high_cut_significance=True)
stack('jet_count_trilepton', ax1, 'Jet Multiplicity ($N_{lep}=3$)', cls=StackHistWithSignificance, high_cut_significance=True)
stack('jet_count_eem_trilepton', ax2, 'Jet Multiplicity ($e^+e^-\mu$)', cls=StackHistWithSignificance, high_cut_significance=True)
stack('jet_count_mme_trilepton', ax3, 'Jet Multiplicity ($\mu^+\mu^-e$)', cls=StackHistWithSignificance, high_cut_significance=True)
fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(2,2, figsize=(26,20), tight_layout=False)
stack('b_jet_count', ax0, 'B-Jet Multiplicity', cls=StackHistWithSignificance, high_cut_significance=True)
stack('b_jet_count_trilepton', ax1, 'B-Jet Multiplicity ($N_{lep}=3$)', cls=StackHistWithSignificance, high_cut_significance=True)
stack('b_jet_count_eem_trilepton', ax2, 'B-Jet Multiplicity ($e^+e^-\mu$)', cls=StackHistWithSignificance, high_cut_significance=True)
stack('b_jet_count_mme_trilepton', ax3, 'B-Jet Multiplicity ($\mu^+\mu^-e$)', cls=StackHistWithSignificance, high_cut_significance=True)<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '407230', 'license': 'MIT', 'n_tokens_mistral': 1091, 'n_tokens_neox': 1034, 'n_words': 204, 'path': 'python/Plotter Testing.ipynb', 'repo_name': 'cfangmeier/TTTT', 'size': '2850'}