diff --git a/include/Rivet/Tools/SmearingFunctions.hh b/include/Rivet/Tools/SmearingFunctions.hh --- a/include/Rivet/Tools/SmearingFunctions.hh +++ b/include/Rivet/Tools/SmearingFunctions.hh @@ -1,734 +1,734 @@ // -*- C++ -*- #ifndef RIVET_SmearingFunctions_HH #define RIVET_SmearingFunctions_HH #include "Rivet/Tools/MomentumSmearingFunctions.hh" #include "Rivet/Tools/ParticleSmearingFunctions.hh" #include "Rivet/Tools/JetSmearingFunctions.hh" namespace Rivet { /// @name Electron efficiency and smearing functions //@{ /// ATLAS Run 1 electron reconstruction efficiency /// @todo Include reco eff (but no e/y discrimination) in forward region /// @todo How to use this in combination with tracking eff? inline double ELECTRON_EFF_ATLAS_RUN1(const Particle& e) { if (e.abseta() > 2.5) return 0; if (e.pT() < 10*GeV) return 0; return (e.abseta() < 1.5) ? 0.95 : 0.85; } /// ATLAS Run 2 electron reco efficiency /// @todo Currently just a copy of Run 1: fix! inline double ELECTRON_EFF_ATLAS_RUN2(const Particle& e) { return ELECTRON_EFF_ATLAS_RUN1(e); } /// @brief ATLAS Run 2 'loose' electron identification/selection efficiency /// /// Values read from Fig 3 of ATL-PHYS-PUB-2015-041 /// @todo What about faking by jets or non-electrons? inline double ELECTRON_IDEFF_ATLAS_RUN2_LOOSE(const Particle& e) { // Manually symmetrised eta eff histogram const static vector edges_eta = { 0.0, 0.1, 0.8, 1.37, 1.52, 2.01, 2.37, 2.47 }; const static vector effs_eta = { 0.950, 0.965, 0.955, 0.885, 0.950, 0.935, 0.90 }; // Et eff histogram (10-20 is a guess) const static vector edges_et = { 0, 10, 20, 25, 30, 35, 40, 45, 50, 60, 80 }; const static vector effs_et = { 0.0, 0.90, 0.91, 0.92, 0.94, 0.95, 0.955, 0.965, 0.97, 0.98 }; if (e.abseta() > 2.47) return 0.0; // no ID outside the tracker const int i_eta = binIndex(e.abseta(), edges_eta); const int i_et = binIndex(e.Et()/GeV, edges_et, true); const double eff = effs_et[i_et] * effs_eta[i_eta] / 0.95; //< norm factor as approximate double differential return min(eff, 1.0); } /// @brief ATLAS Run 1 'medium' electron identification/selection efficiency inline double ELECTRON_IDEFF_ATLAS_RUN1_MEDIUM(const Particle& e) { const static vector eta_edges_10 = {0.000, 0.049, 0.454, 1.107, 1.46, 1.790, 2.277, 2.500}; const static vector eta_vals_10 = {0.730, 0.757, 0.780, 0.771, 0.77, 0.777, 0.778}; const static vector eta_edges_15 = {0.000, 0.053, 0.456, 1.102, 1.463, 1.783, 2.263, 2.500}; const static vector eta_vals_15 = {0.780, 0.800, 0.819, 0.759, 0.749, 0.813, 0.829}; const static vector eta_edges_20 = {0.000, 0.065, 0.362, 0.719, 0.980, 1.289, 1.455, 1.681, 1.942, 2.239, 2.452, 2.500}; const static vector eta_vals_20 = {0.794, 0.806, 0.816, 0.806, 0.797, 0.774, 0.764, 0.788, 0.793, 0.806, 0.825}; const static vector eta_edges_25 = {0.000, 0.077, 0.338, 0.742, 1.004, 1.265, 1.467, 1.692, 1.940, 2.227, 2.452, 2.500}; const static vector eta_vals_25 = {0.833, 0.843, 0.853, 0.845, 0.839, 0.804, 0.790, 0.825, 0.830, 0.833, 0.839}; const static vector eta_edges_30 = {0.000, 0.077, 0.350, 0.707, 0.980, 1.289, 1.479, 1.681, 1.942, 2.239, 2.441, 2.500}; const static vector eta_vals_30 = {0.863, 0.872, 0.881, 0.874, 0.870, 0.824, 0.808, 0.847, 0.845, 0.840, 0.842}; const static vector eta_edges_35 = {0.000, 0.058, 0.344, 0.700, 1.009, 1.270, 1.458, 1.685, 1.935, 2.231, 2.468, 2.500}; const static vector eta_vals_35 = {0.878, 0.889, 0.901, 0.895, 0.893, 0.849, 0.835, 0.868, 0.863, 0.845, 0.832}; const static vector eta_edges_40 = {0.000, 0.047, 0.355, 0.699, 0.983, 1.280, 1.446, 1.694, 1.943, 2.227, 2.441, 2.500}; const static vector eta_vals_40 = {0.894, 0.901, 0.909, 0.905, 0.904, 0.875, 0.868, 0.889, 0.876, 0.848, 0.827}; const static vector eta_edges_45 = {0.000, 0.058, 0.356, 0.712, 0.997, 1.282, 1.459, 1.686, 1.935, 2.220, 2.444, 2.500}; const static vector eta_vals_45 = {0.900, 0.911, 0.923, 0.918, 0.917, 0.897, 0.891, 0.904, 0.894, 0.843, 0.796}; const static vector eta_edges_50 = {0.000, 0.059, 0.355, 0.711, 0.983, 1.280, 1.469, 1.682, 1.919, 2.227, 2.441, 2.500}; const static vector eta_vals_50 = {0.903, 0.913, 0.923, 0.922, 0.923, 0.903, 0.898, 0.908, 0.895, 0.831, 0.774}; const static vector eta_edges_60 = {0.000, 0.053, 0.351, 0.720, 1.006, 1.291, 1.469, 1.696, 1.946, 2.243, 2.455, 2.500}; const static vector eta_vals_60 = {0.903, 0.917, 0.928, 0.924, 0.927, 0.915, 0.911, 0.915, 0.899, 0.827, 0.760}; const static vector eta_edges_80 = {0.000, 0.053, 0.351, 0.720, 0.994, 1.292, 1.482, 1.708, 1.934, 2.220, 2.458, 2.500}; const static vector eta_vals_80 = {0.936, 0.942, 0.952, 0.956, 0.956, 0.934, 0.931, 0.944, 0.933, 0.940, 0.948}; const static vector et_edges = { 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 80 }; const static vector< vector > et_eta_edges = { eta_edges_10, eta_edges_15, eta_edges_20, eta_edges_25, eta_edges_30, eta_edges_35, eta_edges_40, eta_edges_45, eta_edges_50, eta_edges_60, eta_edges_80 }; const static vector< vector > et_eta_vals = { eta_vals_10, eta_vals_15, eta_vals_20, eta_vals_25, eta_vals_30, eta_vals_35, eta_vals_40, eta_vals_45, eta_vals_50, eta_vals_60, eta_vals_80 }; if (e.abseta() > 2.5 || e.Et() < 10*GeV) return 0.0; const int i_et = binIndex(e.Et()/GeV, et_edges, true); const int i_eta = binIndex(e.abseta(), et_eta_edges[i_et]); return et_eta_vals[i_et][i_eta]; } /// @brief ATLAS Run 2 'medium' electron identification/selection efficiency /// @todo Currently just a copy of Run 1: fix! inline double ELECTRON_IDEFF_ATLAS_RUN2_MEDIUM(const Particle& e) { return ELECTRON_IDEFF_ATLAS_RUN1_MEDIUM(e); } /// @brief ATLAS Run 1 'tight' electron identification/selection efficiency inline double ELECTRON_IDEFF_ATLAS_RUN1_TIGHT(const Particle& e) { const static vector eta_edges_10 = {0.000, 0.049, 0.459, 1.100, 1.461, 1.789, 2.270, 2.500}; const static vector eta_vals_10 = {0.581, 0.632, 0.668, 0.558, 0.548, 0.662, 0.690}; const static vector eta_edges_15 = {0.000, 0.053, 0.450, 1.096, 1.463, 1.783, 2.269, 2.500}; const static vector eta_vals_15 = {0.630, 0.678, 0.714, 0.633, 0.616, 0.700, 0.733}; const static vector eta_edges_20 = {0.000, 0.065, 0.362, 0.719, 0.992, 1.277, 1.479, 1.692, 1.930, 2.227, 2.464, 2.500}; const static vector eta_vals_20 = {0.653, 0.695, 0.735, 0.714, 0.688, 0.635, 0.625, 0.655, 0.680, 0.691, 0.674}; const static vector eta_edges_25 = {0.000, 0.077, 0.362, 0.719, 0.992, 1.300, 1.479, 1.692, 1.942, 2.227, 2.464, 2.500}; const static vector eta_vals_25 = {0.692, 0.732, 0.768, 0.750, 0.726, 0.677, 0.667, 0.692, 0.710, 0.706, 0.679}; const static vector eta_edges_30 = {0.000, 0.053, 0.362, 0.719, 1.004, 1.277, 1.467, 1.681, 1.954, 2.239, 2.452, 2.500}; const static vector eta_vals_30 = {0.724, 0.763, 0.804, 0.789, 0.762, 0.702, 0.690, 0.720, 0.731, 0.714, 0.681}; const static vector eta_edges_35 = {0.000, 0.044, 0.342, 0.711, 0.971, 1.280, 1.456, 1.683, 1.944, 2.218, 2.442, 2.500}; const static vector eta_vals_35 = {0.736, 0.778, 0.824, 0.811, 0.784, 0.730, 0.718, 0.739, 0.743, 0.718, 0.678}; const static vector eta_edges_40 = {0.000, 0.047, 0.355, 0.699, 0.983, 1.268, 1.457, 1.671, 1.931, 2.204, 2.453, 2.500}; const static vector eta_vals_40 = {0.741, 0.774, 0.823, 0.823, 0.802, 0.764, 0.756, 0.771, 0.771, 0.734, 0.684}; const static vector eta_edges_45 = {0.000, 0.056, 0.354, 0.711, 0.984, 1.280, 1.458, 1.684, 1.945, 2.207, 2.442, 2.500}; const static vector eta_vals_45 = {0.758, 0.792, 0.841, 0.841, 0.823, 0.792, 0.786, 0.796, 0.794, 0.734, 0.663}; const static vector eta_edges_50 = {0.000, 0.059, 0.355, 0.699, 0.983, 1.268, 1.446, 1.682, 1.943, 2.216, 2.453, 2.500}; const static vector eta_vals_50 = {0.771, 0.806, 0.855, 0.858, 0.843, 0.810, 0.800, 0.808, 0.802, 0.730, 0.653}; const static vector eta_edges_60 = {0.000, 0.050, 0.350, 0.707, 0.981, 1.278, 1.468, 1.694, 1.944, 2.242, 2.453, 2.500}; const static vector eta_vals_60 = {0.773, 0.816, 0.866, 0.865, 0.853, 0.820, 0.812, 0.817, 0.804, 0.726, 0.645}; const static vector eta_edges_80 = {0.000, 0.051, 0.374, 0.720, 0.981, 1.279, 1.468, 1.707, 1.945, 2.207, 2.457, 2.500}; const static vector eta_vals_80 = {0.819, 0.855, 0.899, 0.906, 0.900, 0.869, 0.865, 0.873, 0.869, 0.868, 0.859}; const static vector et_edges = { 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 80 }; const static vector< vector > et_eta_edges = { eta_edges_10, eta_edges_15, eta_edges_20, eta_edges_25, eta_edges_30, eta_edges_35, eta_edges_40, eta_edges_45, eta_edges_50, eta_edges_60, eta_edges_80 }; const static vector< vector > et_eta_vals = { eta_vals_10, eta_vals_15, eta_vals_20, eta_vals_25, eta_vals_30, eta_vals_35, eta_vals_40, eta_vals_45, eta_vals_50, eta_vals_60, eta_vals_80 }; if (e.abseta() > 2.5 || e.Et() < 10*GeV) return 0.0; const int i_et = binIndex(e.Et()/GeV, et_edges, true); const int i_eta = binIndex(e.abseta(), et_eta_edges[i_et]); return et_eta_vals[i_et][i_eta]; } /// @brief ATLAS Run 2 'tight' electron identification/selection efficiency /// @todo Currently just a copy of Run 1: fix! inline double ELECTRON_IDEFF_ATLAS_RUN2_TIGHT(const Particle& e) { return ELECTRON_IDEFF_ATLAS_RUN1_TIGHT(e); } /// ATLAS Run 1 electron reco smearing inline Particle ELECTRON_SMEAR_ATLAS_RUN1(const Particle& e) { static const vector edges_eta = {0., 2.5, 3.}; static const vector edges_pt = {0., 0.1, 25.}; static const vector e2s = {0.000, 0.015, 0.005, 0.005, 0.005, 0.005, 0.107, 0.107, 0.107}; static const vector es = {0.00, 0.00, 0.05, 0.05, 0.05, 0.05, 2.08, 2.08, 2.08}; static const vector cs = {0.00, 0.00, 0.25, 0.25, 0.25, 0.25, 0.00, 0.00, 0.00}; const int i_eta = binIndex(e.abseta(), edges_eta, true); const int i_pt = binIndex(e.pT()/GeV, edges_pt, true); const int i = i_eta*edges_pt.size() + i_pt; // Calculate absolute resolution in GeV const double c1 = sqr(e2s[i]), c2 = sqr(es[i]), c3 = sqr(cs[i]); const double resolution = sqrt(c1*e.E2() + c2*e.E() + c3) * GeV; // normal_distribution<> d(e.E(), resolution); // const double mass = e.mass2() > 0 ? e.mass() : 0; //< numerical carefulness... // const double smeared_E = max(d(gen), mass); //< can't let the energy go below the mass! // return Particle(e.pid(), FourMomentum::mkEtaPhiME(e.eta(), e.phi(), mass, smeared_E)); return Particle(e.pid(), P4_SMEAR_E_GAUSS(e, resolution)); } /// ATLAS Run 2 electron reco smearing /// @todo Currently just a copy of the Run 1 version: fix! inline Particle ELECTRON_SMEAR_ATLAS_RUN2(const Particle& e) { return ELECTRON_SMEAR_ATLAS_RUN1(e); } /// @todo Add charge flip efficiency? /// CMS Run 1 electron reconstruction efficiency inline double ELECTRON_EFF_CMS_RUN1(const Particle& e) { if (e.abseta() > 2.5) return 0; if (e.pT() < 10*GeV) return 0; return (e.abseta() < 1.5) ? 0.95 : 0.85; } /// CMS Run 2 electron reco efficiency /// @todo Currently just a copy of Run 1: fix! inline double ELECTRON_EFF_CMS_RUN2(const Particle& e) { return ELECTRON_EFF_CMS_RUN1(e); } /// @brief CMS electron energy smearing, preserving direction /// /// Calculate resolution /// for pT > 0.1 GeV, E resolution = |eta| < 0.5 -> sqrt(0.06^2 + pt^2 * 1.3e-3^2) /// |eta| < 1.5 -> sqrt(0.10^2 + pt^2 * 1.7e-3^2) /// |eta| < 2.5 -> sqrt(0.25^2 + pt^2 * 3.1e-3^2) inline Particle ELECTRON_SMEAR_CMS_RUN1(const Particle& e) { // Calculate absolute resolution in GeV from functional form double resolution = 0; const double abseta = e.abseta(); if (e.pT() > 0.1*GeV && abseta < 2.5) { //< should be a given from efficiencies if (abseta < 0.5) { resolution = add_quad(0.06, 1.3e-3 * e.pT()/GeV) * GeV; } else if (abseta < 1.5) { resolution = add_quad(0.10, 1.7e-3 * e.pT()/GeV) * GeV; } else { // still |eta| < 2.5 resolution = add_quad(0.25, 3.1e-3 * e.pT()/GeV) * GeV; } } // normal_distribution<> d(e.E(), resolution); // const double mass = e.mass2() > 0 ? e.mass() : 0; //< numerical carefulness... // const double smeared_E = max(d(gen), mass); //< can't let the energy go below the mass! // return Particle(e.pid(), FourMomentum::mkEtaPhiME(e.eta(), e.phi(), mass, smeared_E)); return Particle(e.pid(), P4_SMEAR_E_GAUSS(e, resolution)); } /// CMS Run 2 electron reco smearing /// @todo Currently just a copy of the Run 1 version: fix! inline Particle ELECTRON_SMEAR_CMS_RUN2(const Particle& e) { return ELECTRON_SMEAR_CMS_RUN1(e); } //@} /// @name Photon efficiency and smearing functions //@{ /// @brief ATLAS Run 2 photon reco efficiency /// /// Taken from converted photons, Fig 8, in arXiv:1606.01813 inline double PHOTON_EFF_ATLAS_RUN1(const Particle& y) { if (y.pT() < 10*GeV) return 0; if (inRange(y.abseta(), 1.37, 1.52) || y.abseta() > 2.37) return 0; static const vector edges_eta = {0., 0.6, 1.37, 1.52, 1.81, 2.37}; static const vector edges_pt = {10., 15., 20., 25., 30., 35., 40., 45., 50., 60., 80., 100., 125., 150., 175., 250.}; static const vector effs = {0.53, 0.65, 0.73, 0.83, 0.86, 0.93, 0.94, 0.96, 0.97, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98,// 0.45, 0.57, 0.67, 0.74, 0.84, 0.87, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.98, 0.99, 0.99, 0.99,// 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,// 0.48, 0.56, 0.68, 0.76, 0.86, 0.90, 0.93, 0.95, 0.96, 0.97, 0.98, 0.99, 0.99, 1.00, 1.00, 1.00,// 0.50, 0.61, 0.74, 0.82, 0.88, 0.92, 0.94, 0.95, 0.96, 0.97, 0.98, 0.98, 0.98, 0.98, 0.99, 0.99}; const int i_eta = binIndex(y.abseta(), edges_eta); const int i_pt = binIndex(y.pT()/GeV, edges_pt, true); const int i = i_eta*edges_pt.size() + i_pt; const double eff = effs[i]; return eff; } /// @brief ATLAS Run 2 photon reco efficiency /// /// Taken from converted photons, Fig 6, in ATL-PHYS-PUB-2016-014 inline double PHOTON_EFF_ATLAS_RUN2(const Particle& y) { if (y.pT() < 10*GeV) return 0; if (inRange(y.abseta(), 1.37, 1.52) || y.abseta() > 2.37) return 0; static const vector edges_eta = {0., 0.6, 1.37, 1.52, 1.81, 2.37}; static const vector edges_pt = {10., 15., 20., 25., 30., 35., 40., 45., 50., 60., 80., 100., 125., 150., 175., 250.}; static const vector effs = {0.55, 0.70, 0.85, 0.89, 0.93, 0.95, 0.96, 0.96, 0.97, 0.97, 0.98, 0.97, 0.97, 0.97, 0.97, 0.97,// 0.47, 0.66, 0.79, 0.86, 0.89, 0.94, 0.96, 0.97, 0.97, 0.98, 0.97, 0.98, 0.98, 0.98, 0.98, 0.98,// 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,// 0.54, 0.71, 0.84, 0.88, 0.92, 0.93, 0.94, 0.95, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96,// 0.61, 0.74, 0.83, 0.88, 0.91, 0.94, 0.95, 0.96, 0.97, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98}; const int i_eta = binIndex(y.abseta(), edges_eta); const int i_pt = binIndex(y.pT()/GeV, edges_pt, true); const int i = i_eta*edges_pt.size() + i_pt; const double eff = effs[i]; return eff; } /// CMS Run 1 photon reco efficiency /// @todo Currently from Delphes inline double PHOTON_EFF_CMS_RUN1(const Particle& y) { if (y.pT() < 10*GeV || y.abseta() > 2.5) return 0; return (y.abseta() < 1.5) ? 0.95 : 0.85; } /// CMS Run 2 photon reco efficiency /// @todo Currently just a copy of Run 1: fix! inline double PHOTON_EFF_CMS_RUN2(const Particle& y) { return PHOTON_EFF_CMS_RUN1(y); } /// @todo Use real photon smearing inline Particle PHOTON_SMEAR_ATLAS_RUN1(const Particle& y) { return y; } inline Particle PHOTON_SMEAR_ATLAS_RUN2(const Particle& y) { return y; } inline Particle PHOTON_SMEAR_CMS_RUN1(const Particle& y) { return y; } inline Particle PHOTON_SMEAR_CMS_RUN2(const Particle& y) { return y; } //@} /// @name Muon efficiency and smearing functions //@{ /// ATLAS Run 1 muon reco efficiency inline double MUON_EFF_ATLAS_RUN1(const Particle& m) { if (m.abseta() > 2.7) return 0; if (m.pT() < 10*GeV) return 0; - return (m.abseta() < 1.5) ? 0..95 : 0.85; + return (m.abseta() < 1.5) ? 0.95 : 0.85; } /// ATLAS Run 2 muon reco efficiency /// @todo Currently just a copy of Run 1: fix! inline double MUON_EFF_ATLAS_RUN2(const Particle& m) { return MUON_EFF_ATLAS_RUN1(m); } /// ATLAS Run 1 muon reco smearing inline Particle MUON_SMEAR_ATLAS_RUN1(const Particle& m) { static const vector edges_eta = {0, 1.5, 2.5}; static const vector edges_pt = {0, 0.1, 1.0, 10., 200.}; static const vector res = {0., 0.03, 0.02, 0.03, 0.05, 0., 0.04, 0.03, 0.04, 0.05}; const int i_eta = binIndex(m.abseta(), edges_eta); const int i_pt = binIndex(m.pT()/GeV, edges_pt, true); const int i = i_eta*edges_pt.size() + i_pt; const double resolution = res[i]; // Smear by a Gaussian centered on the current pT, with width given by the resolution // normal_distribution<> d(m.pT(), resolution*m.pT()); // const double smeared_pt = max(d(gen), 0.); // const double mass = m.mass2() > 0 ? m.mass() : 0; //< numerical carefulness... // return Particle(m.pid(), FourMomentum::mkEtaPhiMPt(m.eta(), m.phi(), mass, smeared_pt)); return Particle(m.pid(), P4_SMEAR_PT_GAUSS(m, resolution*m.pT())); } /// ATLAS Run 2 muon reco smearing /// @todo Currently just a copy of the Run 1 version: fix! inline Particle MUON_SMEAR_ATLAS_RUN2(const Particle& m) { return MUON_SMEAR_ATLAS_RUN1(m); } /// CMS Run 1 muon reco efficiency inline double MUON_EFF_CMS_RUN1(const Particle& m) { if (m.abseta() > 2.4) return 0; if (m.pT() < 10*GeV) return 0; return 0.95 * (m.abseta() < 1.5 ? 1 : exp(0.5 - 5e-4*m.pT()/GeV)); } /// CMS Run 2 muon reco efficiency /// @todo Currently just a copy of Run 1: fix! inline double MUON_EFF_CMS_RUN2(const Particle& m) { return MUON_EFF_CMS_RUN1(m); } /// CMS Run 1 muon reco smearing inline Particle MUON_SMEAR_CMS_RUN1(const Particle& m) { // Calculate fractional resolution // for pT > 0.1 GeV, mom resolution = |eta| < 0.5 -> sqrt(0.01^2 + pt^2 * 2.0e-4^2) // |eta| < 1.5 -> sqrt(0.02^2 + pt^2 * 3.0e-4^2) // |eta| < 2.5 -> sqrt(0.05^2 + pt^2 * 2.6e-4^2) double resolution = 0; const double abseta = m.abseta(); if (m.pT() > 0.1*GeV && abseta < 2.5) { if (abseta < 0.5) { resolution = add_quad(0.01, 2.0e-4 * m.pT()/GeV); } else if (abseta < 1.5) { resolution = add_quad(0.02, 3.0e-4 * m.pT()/GeV); } else { // still |eta| < 2.5... but isn't CMS' mu acceptance < 2.4? resolution = add_quad(0.05, 2.6e-4 * m.pT()/GeV); } } // Smear by a Gaussian centered on the current pT, with width given by the resolution // normal_distribution<> d(m.pT(), resolution*m.pT()); // const double smeared_pt = max(d(gen), 0.); // const double mass = m.mass2() > 0 ? m.mass() : 0; //< numerical carefulness... // return Particle(m.pid(), FourMomentum::mkEtaPhiMPt(m.eta(), m.phi(), mass, smeared_pt)); return Particle(m.pid(), P4_SMEAR_PT_GAUSS(m, resolution*m.pT())); } /// CMS Run 2 muon reco smearing /// @todo Currently just a copy of the Run 1 version: fix! inline Particle MUON_SMEAR_CMS_RUN2(const Particle& m) { return MUON_SMEAR_CMS_RUN1(m); } //@} /// @name Tau efficiency and smearing functions //@{ /// @brief ATLAS Run 1 8 TeV tau efficiencies (medium working point) /// /// Taken from http://arxiv.org/pdf/1412.7086.pdf /// 20-40 GeV 1-prong LMT eff|mis = 0.66|1/10, 0.56|1/20, 0.36|1/80 /// 20-40 GeV 3-prong LMT eff|mis = 0.45|1/60, 0.38|1/100, 0.27|1/300 /// > 40 GeV 1-prong LMT eff|mis = 0.66|1/15, 0.56|1/25, 0.36|1/80 /// > 40 GeV 3-prong LMT eff|mis = 0.45|1/250, 0.38|1/400, 0.27|1/1300 inline double TAU_EFF_ATLAS_RUN1(const Particle& t) { if (t.abseta() > 2.5) return 0; //< hmm... mostly double pThadvis = 0; Particles chargedhadrons; for (const Particle& p : t.children()) { if (p.isHadron()) { pThadvis += p.pT(); //< right definition? Paper is unclear if (p.charge3() != 0 && p.abseta() < 2.5 && p.pT() > 1*GeV) chargedhadrons += p; } } if (chargedhadrons.empty()) return 0; //< leptonic tau if (pThadvis < 20*GeV) return 0; //< below threshold if (pThadvis < 40*GeV) { if (chargedhadrons.size() == 1) return (t.abspid() == PID::TAU) ? 0.56 : 1/20.; if (chargedhadrons.size() == 3) return (t.abspid() == PID::TAU) ? 0.38 : 1/100.; } else { if (chargedhadrons.size() == 1) return (t.abspid() == PID::TAU) ? 0.56 : 1/25.; if (chargedhadrons.size() == 3) return (t.abspid() == PID::TAU) ? 0.38 : 1/400.; } return 0; } /// @brief ATLAS Run 2 13 TeV tau efficiencies (medium working point) /// /// From https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2015-045/ATL-PHYS-PUB-2015-045.pdf /// LMT 1 prong efficiency/mistag = 0.6|1/30, 0.55|1/50, 0.45|1/120 /// LMT 3 prong efficiency/mistag = 0.5|1/30, 0.4|1/110, 0.3|1/300 inline double TAU_EFF_ATLAS_RUN2(const Particle& t) { if (t.abseta() > 2.5) return 0; //< hmm... mostly double pThadvis = 0; Particles chargedhadrons; for (const Particle& p : t.children()) { if (p.isHadron()) { pThadvis += p.pT(); //< right definition? Paper is unclear if (p.charge3() != 0 && p.abseta() < 2.5 && p.pT() > 1*GeV) chargedhadrons += p; } } if (chargedhadrons.empty()) return 0; //< leptonic tau if (pThadvis < 20*GeV) return 0; //< below threshold if (chargedhadrons.size() == 1) return (t.abspid() == PID::TAU) ? 0.55 : 1/50.; if (chargedhadrons.size() == 3) return (t.abspid() == PID::TAU) ? 0.40 : 1/110.; return 0; } /// ATLAS Run 1 tau smearing /// @todo Currently a copy of the crappy jet smearing that is probably wrong... inline Particle TAU_SMEAR_ATLAS_RUN1(const Particle& t) { // Const fractional resolution for now static const double resolution = 0.03; // Smear by a Gaussian centered on 1 with width given by the (fractional) resolution /// @todo Is this the best way to smear? Should we preserve the energy, or pT, or direction? const double fsmear = max(randnorm(1., resolution), 0.); const double mass = t.mass2() > 0 ? t.mass() : 0; //< numerical carefulness... return Particle(t.pid(), FourMomentum::mkXYZM(t.px()*fsmear, t.py()*fsmear, t.pz()*fsmear, mass)); } /// ATLAS Run 2 tau smearing /// @todo Currently a copy of the Run 1 version inline Particle TAU_SMEAR_ATLAS_RUN2(const Particle& t) { return TAU_SMEAR_ATLAS_RUN1(t); } /// CMS Run 2 tau efficiency /// /// @todo Needs work; this is the dumb version from Delphes 3.3.2 inline double TAU_EFF_CMS_RUN2(const Particle& t) { return (t.abspid() == PID::TAU) ? 0.6 : 0; } /// CMS Run 1 tau efficiency /// /// @todo Needs work; this is just a copy of the Run 2 version in Delphes 3.3.2 inline double TAU_EFF_CMS_RUN1(const Particle& t) { return TAU_EFF_CMS_RUN2(t); } /// CMS Run 1 tau smearing /// @todo Currently a copy of the crappy ATLAS one inline Particle TAU_SMEAR_CMS_RUN1(const Particle& t) { return TAU_SMEAR_ATLAS_RUN1(t); } /// CMS Run 2 tau smearing /// @todo Currently a copy of the Run 1 version inline Particle TAU_SMEAR_CMS_RUN2(const Particle& t) { return TAU_SMEAR_CMS_RUN1(t); } //@} /// @name Jet efficiency and smearing functions //@{ /// Return the ATLAS Run 1 jet flavour tagging efficiency for the given Jet inline double JET_BTAG_ATLAS_RUN1(const Jet& j) { /// @todo This form drops past ~100 GeV, asymptotically to zero efficiency... really?! if (j.abseta() > 2.5) return 0; const auto ftagsel = [&](const Particle& p){ return p.pT() > 5*GeV && deltaR(p,j) < 0.3; }; if (j.bTagged(ftagsel)) return 0.80*tanh(0.003*j.pT()/GeV)*(30/(1+0.0860*j.pT()/GeV)); if (j.cTagged(ftagsel)) return 0.20*tanh(0.020*j.pT()/GeV)*( 1/(1+0.0034*j.pT()/GeV)); return 0.002 + 7.3e-6*j.pT()/GeV; } /// Return the ATLAS Run 2 MC2c20 jet flavour tagging efficiency for the given Jet inline double JET_BTAG_ATLAS_RUN2_MV2C20(const Jet& j) { if (j.abseta() > 2.5) return 0; if (j.bTagged(Cuts::pT > 5*GeV)) return 0.77; if (j.cTagged(Cuts::pT > 5*GeV)) return 1/4.5; return 1/140.; } /// Return the ATLAS Run 2 MC2c10 jet flavour tagging efficiency for the given Jet inline double JET_BTAG_ATLAS_RUN2_MV2C10(const Jet& j) { if (j.abseta() > 2.5) return 0; if (j.bTagged(Cuts::pT > 5*GeV)) return 0.77; if (j.cTagged(Cuts::pT > 5*GeV)) return 1/6.0; return 1/134.; } /// ATLAS Run 1 jet smearing inline Jet JET_SMEAR_ATLAS_RUN1(const Jet& j) { // Jet energy resolution lookup // Implemented by Matthias Danninger for GAMBIT, based roughly on // https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2015-017/ // Parameterisation can be still improved, but eta dependence is minimal /// @todo Also need a JES uncertainty component? static const vector binedges_pt = {0., 50., 70., 100., 150., 200., 1000., 10000.}; static const vector jer = {0.145, 0.115, 0.095, 0.075, 0.07, 0.05, 0.04, 0.04}; //< note overflow value const int ipt = binIndex(j.pT()/GeV, binedges_pt, true); if (ipt < 0) return j; const double resolution = jer.at(ipt); // Smear by a Gaussian centered on 1 with width given by the (fractional) resolution /// @todo Is this the best way to smear? Should we preserve the energy, or pT, or direction? const double fsmear = max(randnorm(1., resolution), 0.); const double mass = j.mass2() > 0 ? j.mass() : 0; //< numerical carefulness... Jet rtn(FourMomentum::mkXYZM(j.px()*fsmear, j.py()*fsmear, j.pz()*fsmear, mass)); //if (deltaPhi(j, rtn) > 0.01) cout << "jdphi: " << deltaPhi(j, rtn) << endl; return rtn; } /// ATLAS Run 2 jet smearing /// @todo Just a copy of the Run 1 one: improve!! inline Jet JET_SMEAR_ATLAS_RUN2(const Jet& j) { return JET_SMEAR_ATLAS_RUN1(j); } /// CMS Run 2 jet smearing /// @todo Just a copy of the suboptimal ATLAS one: improve!! inline Jet JET_SMEAR_CMS_RUN2(const Jet& j) { return JET_SMEAR_ATLAS_RUN1(j); } //@} /// @name ETmiss smearing functions //@{ inline Vector3 MET_SMEAR_IDENTITY(const Vector3& met, double) { return met; } /// @brief ATLAS Run 1 ETmiss smearing /// /// Based on https://arxiv.org/pdf/1108.5602v2.pdf, Figs 14 and 15 inline Vector3 MET_SMEAR_ATLAS_RUN1(const Vector3& met, double set) { // Linearity offset (Fig 14) Vector3 smeared_met = met; if (met.mod()/GeV < 25*GeV) smeared_met *= 1.05; else if (met.mod()/GeV < 40*GeV) smeared_met *= (1.05 - (0.04/15)*(met.mod()/GeV - 25)); //< linear decrease else smeared_met *= 1.01; // Smear by a Gaussian with width given by the resolution(sumEt) ~ 0.45 sqrt(sumEt) GeV const double resolution = 0.45 * sqrt(set/GeV) * GeV; const double metsmear = max(randnorm(smeared_met.mod(), resolution), 0.); smeared_met = metsmear * smeared_met.unit(); return smeared_met; } /// ATLAS Run 2 ETmiss smearing /// @todo Just a copy of the Run 1 one: improve!! inline Vector3 MET_SMEAR_ATLAS_RUN2(const Vector3& met, double set) { return MET_SMEAR_ATLAS_RUN1(met, set); } /// CMS Run 1 ETmiss smearing /// @todo Just a copy of the ATLAS one: improve!! inline Vector3 MET_SMEAR_CMS_RUN1(const Vector3& met, double set) { return MET_SMEAR_ATLAS_RUN1(met, set); } /// CMS Run 2 ETmiss smearing /// @todo Just a copy of the ATLAS one: improve!! inline Vector3 MET_SMEAR_CMS_RUN2(const Vector3& met, double set) { return MET_SMEAR_ATLAS_RUN2(met, set); } //@} /// @name Tracking efficiency and smearing functions //@{ /// ATLAS Run 1 tracking efficiency inline double TRK_EFF_ATLAS_RUN1(const Particle& p) { if (p.charge3() == 0) return 0; if (p.abseta() > 2.5) return 0; if (p.pT() < 0.1*GeV) return 0; if (p.abspid() == PID::ELECTRON) { if (p.abseta() < 1.5) { if (p.pT() < 1*GeV) return 0.73; if (p.pT() < 100*GeV) return 0.95; return 0.99; } else { if (p.pT() < 1*GeV) return 0.50; if (p.pT() < 100*GeV) return 0.83; else return 0.90; } } else if (p.abspid() == PID::MUON) { if (p.abseta() < 1.5) { return (p.pT() < 1*GeV) ? 0.75 : 0.99; } else { return (p.pT() < 1*GeV) ? 0.70 : 0.98; } } else { // charged hadrons if (p.abseta() < 1.5) { return (p.pT() < 1*GeV) ? 0.70 : 0.95; } else { return (p.pT() < 1*GeV) ? 0.60 : 0.85; } } } /// ATLAS Run 2 tracking efficiency /// @todo Currently just a copy of Run 1: fix! inline double TRK_EFF_ATLAS_RUN2(const Particle& p) { return TRK_EFF_ATLAS_RUN1(p); } /// CMS Run 1 tracking efficiency inline double TRK_EFF_CMS_RUN1(const Particle& p) { if (p.charge3() == 0) return 0; if (p.abseta() > 2.5) return 0; if (p.pT() < 0.1*GeV) return 0; if (p.abspid() == PID::ELECTRON) { if (p.abseta() < 1.5) { if (p.pT() < 1*GeV) return 0.73; if (p.pT() < 100*GeV) return 0.95; return 0.99; } else { if (p.pT() < 1*GeV) return 0.50; if (p.pT() < 100*GeV) return 0.83; else return 0.90; } } else if (p.abspid() == PID::MUON) { if (p.abseta() < 1.5) { return (p.pT() < 1*GeV) ? 0.75 : 0.99; } else { return (p.pT() < 1*GeV) ? 0.70 : 0.98; } } else { // charged hadrons if (p.abseta() < 1.5) { return (p.pT() < 1*GeV) ? 0.70 : 0.95; } else { return (p.pT() < 1*GeV) ? 0.60 : 0.85; } } } /// CMS Run 2 tracking efficiency /// @todo Currently just a copy of Run 1: fix! inline double TRK_EFF_CMS_RUN2(const Particle& p) { return TRK_EFF_CMS_RUN1(p); } //@} } #endif