# list of processes (mandatory) processList = { #'p8_ee_ZZ_ecm240':{'fraction':1}, #'p8_ee_WW_ecm240':{'fraction':1}, #'wzp6_ee_mumuH_ecm240':{'fraction':1}, 'p8_ee_WW_mumu_ecm240': {'fraction':1, 'crossSection': 0.25792}, 'p8_ee_ZZ_mumubb_ecm240': {'fraction':1, 'crossSection': 2 * 1.35899 * 0.034 * 0.152}, 'p8_ee_ZH_Zmumu_ecm240': {'fraction':1, 'crossSection': 0.201868 * 0.034}, } # Production tag when running over EDM4Hep centrally produced events, this points to the yaml files for getting sample statistics (mandatory) #prodTag = "FCCee/winter2023/IDEA/" # Link to the dictonary that contains all the cross section informations etc... (mandatory) procDict = "FCCee_procDict_winter2023_IDEA.json" # additional/custom C++ functions, defined in header files (optional) includePaths = ["functions.h"] # Define the input dir (optional) #inputDir = "outputs/FCCee/higgs/mH-recoil/mumu/stage1" inputDir = "./localSamples/" #Optional: output directory, default is local running directory outputDir = "./outputs/histmaker/recoil/" # optional: ncpus, default is 4, -1 uses all cores available nCPUS = -1 # scale the histograms with the cross-section and integrated luminosity doScale = True intLumi = 5000000 # 5 /ab # define some binning for various histograms bins_p_mu = (2000, 0, 200) # 100 MeV bins bins_m_ll = (2000, 0, 200) # 100 MeV bins bins_p_ll = (2000, 0, 200) # 100 MeV bins bins_recoil = (200000, 0, 200) # 1 MeV bins bins_cosThetaMiss = (10000, 0, 1) bins_theta = (500, -5, 5) bins_eta = (600, -3, 3) bins_phi = (500, -5, 5) bins_count = (50, 0, 50) bins_charge = (10, -5, 5) bins_iso = (500, 0, 5) # build_graph function that contains the analysis logic, cuts and histograms (mandatory) def build_graph(df, dataset): results = [] df = df.Define("weight", "1.0") weightsum = df.Sum("weight") # define some aliases to be used later on df = df.Alias("Particle0", "_Particle_daughters.index") df = df.Alias("Particle1", "_Particle_parents.index") df = df.Alias("RecoMCLink0", "_RecoMCLink_from.index") df = df.Alias("RecoMCLink1", "_RecoMCLink_to.index") df = df.Alias("Muon0", "Muon_objIdx.index") # get all the leptons from the collection df = df.Define("muons_all", "FCCAnalyses::ReconstructedParticle::get(Muon0, ReconstructedParticles)") # select leptons with momentum > 20 GeV df = df.Define("muons", "FCCAnalyses::ReconstructedParticle::sel_p(20)(muons_all)") df = df.Define("muons_p", "FCCAnalyses::ReconstructedParticle::get_p(muons)") df = df.Define("muons_theta", "FCCAnalyses::ReconstructedParticle::get_theta(muons)") df = df.Define("muons_phi", "FCCAnalyses::ReconstructedParticle::get_phi(muons)") df = df.Define("muons_q", "FCCAnalyses::ReconstructedParticle::get_charge(muons)") df = df.Define("muons_no", "FCCAnalyses::ReconstructedParticle::get_n(muons)") # compute the muon isolation and store muons with an isolation cut of 0.25 in a separate column muons_sel_iso df = df.Define("muons_iso", "FCCAnalyses::ZHfunctions::coneIsolation(0.01, 0.5)(muons, ReconstructedParticles)") df = df.Define("muons_sel_iso", "FCCAnalyses::ZHfunctions::sel_iso(0.25)(muons, muons_iso)") # baseline histograms, before any selection cuts (store with _cut0) results.append(df.Histo1D(("muons_p_cut0", "", *bins_p_mu), "muons_p")) results.append(df.Histo1D(("muons_theta_cut0", "", *bins_theta), "muons_theta")) results.append(df.Histo1D(("muons_phi_cut0", "", *bins_phi), "muons_phi")) results.append(df.Histo1D(("muons_q_cut0", "", *bins_charge), "muons_q")) results.append(df.Histo1D(("muons_no_cut0", "", *bins_count), "muons_no")) results.append(df.Histo1D(("muons_iso_cut0", "", *bins_iso), "muons_iso")) ######### ### CUT 0: all events ######### df = df.Define("cut0", "0") results.append(df.Histo1D(("cutFlow", "", *bins_count), "cut0")) ######### ### CUT 1: at least 1 muon with at least one isolated one ######### df = df.Filter("muons_no >= 1 && muons_sel_iso.size() > 0") df = df.Define("cut1", "1") results.append(df.Histo1D(("cutFlow", "", *bins_count), "cut1")) ######### ### CUT 2 :at least 2 opposite-sign (OS) leptons ######### df = df.Filter("muons_no >= 2 && abs(Sum(muons_q)) < muons_q.size()") df = df.Define("cut2", "2") results.append(df.Histo1D(("cutFlow", "", *bins_count), "cut2")) # now we build the Z resonance based on the available leptons. # the function resonanceBuilder_mass_recoil returns the best lepton pair compatible with the Z mass (91.2 GeV) and recoil at 125 GeV # the argument 0.4 gives a weight to the Z mass and the recoil mass in the chi2 minimization # technically, it returns a ReconstructedParticleData object with index 0 the di-lepton system, index and 2 the leptons of the pair df = df.Define("zbuilder_result", "FCCAnalyses::ZHfunctions::resonanceBuilder_mass_recoil(91.2, 125, 0.4, 240, false)(muons, RecoMCLink0, RecoMCLink1, ReconstructedParticles, Particle, Particle0, Particle1)") df = df.Define("zmumu", "Vec_rp{zbuilder_result[0]}") # the Z df = df.Define("zmumu_muons", "Vec_rp{zbuilder_result[1],zbuilder_result[2]}") # the leptons df = df.Define("zmumu_m", "FCCAnalyses::ReconstructedParticle::get_mass(zmumu)[0]") # Z mass df = df.Define("zmumu_p", "FCCAnalyses::ReconstructedParticle::get_p(zmumu)[0]") # momentum of the Z df = df.Define("zmumu_recoil", "FCCAnalyses::ReconstructedParticle::recoilBuilder(240)(zmumu)") # compute the recoil based on the reconstructed Z df = df.Define("zmumu_recoil_m", "FCCAnalyses::ReconstructedParticle::get_mass(zmumu_recoil)[0]") # recoil mass df = df.Define("zmumu_muons_p", "FCCAnalyses::ReconstructedParticle::get_p(zmumu_muons)") # get the momentum of the 2 muons from the Z resonance ######### ### CUT 3: Z mass window ######### df = df.Filter("zmumu_m > 86 && zmumu_m < 96") df = df.Define("cut3", "3") results.append(df.Histo1D(("cutFlow", "", *bins_count), "cut3")) ######### ### CUT 4: Z momentum ######### df = df.Filter("zmumu_p > 20 && zmumu_p < 70") df = df.Define("cut4", "4") results.append(df.Histo1D(("cutFlow", "", *bins_count), "cut4")) ######### ### CUT 5: cosThetaMiss ######### df = df.Define("missingEnergy", "FCCAnalyses::ZHfunctions::missingEnergy(240., ReconstructedParticles)") #df = df.Define("cosTheta_miss", "FCCAnalyses::get_cosTheta_miss(missingEnergy)") df = df.Define("cosTheta_miss", "FCCAnalyses::ZHfunctions::get_cosTheta_miss(missingEnergy)") results.append(df.Histo1D(("cosThetaMiss_cut4", "", *bins_cosThetaMiss), "cosTheta_miss")) # plot it before the cut df = df.Filter("cosTheta_miss < 0.98") df = df.Define("cut5", "5") results.append(df.Histo1D(("cutFlow", "", *bins_count), "cut5")) ######### ### CUT 6: recoil mass window ######### df = df.Filter("zmumu_recoil_m < 140 && zmumu_recoil_m > 120") df = df.Define("cut6", "6") results.append(df.Histo1D(("cutFlow", "", *bins_count), "cut6")) ######################## # Final histograms ######################## results.append(df.Histo1D(("zmumu_m", "", *bins_m_ll), "zmumu_m")) results.append(df.Histo1D(("zmumu_recoil_m", "", *bins_recoil), "zmumu_recoil_m")) results.append(df.Histo1D(("zmumu_p", "", *bins_p_ll), "zmumu_p")) results.append(df.Histo1D(("zmumu_muons_p", "", *bins_p_mu), "zmumu_muons_p")) return results, weightsum