file ColliderBit/CMSEfficiencies.hpp
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Namespaces
| Name |
|---|
| Gambit TODO: see if we can use this one: |
| Gambit::ColliderBit |
| Gambit::ColliderBit::CMS CMS-specific efficiency and smearing functions for super fast detector simulation. |
Detailed Description
Author:
- Andy Buckley
- Abram Krislock
- Anders Kvellestad
- Matthias Danninger
- Rose Kudzman-Blais
Functions that do super fast CMS detector simulation based on four-vector smearing.
Authors (add name and date if you modify):
Source code
// GAMBIT: Global and Modular BSM Inference Tool
// *********************************************
///
/// \file
/// Functions that do super fast CMS detector
/// simulation based on four-vector smearing.
///
/// *********************************************
///
/// Authors (add name and date if you modify):
///
/// \author Andy Buckley
/// \author Abram Krislock
/// \author Anders Kvellestad
/// \author Matthias Danninger
/// \author Rose Kudzman-Blais
///
/// *********************************************
#pragma once
#include <cfloat>
#include "gambit/ColliderBit/Utils.hpp"
#include "gambit/Utils/threadsafe_rng.hpp"
#include "HEPUtils/MathUtils.h"
#include "HEPUtils/BinnedFn.h"
#include "HEPUtils/Event.h"
#include <iomanip>
#include <algorithm>
namespace Gambit
{
namespace ColliderBit
{
/// CMS-specific efficiency and smearing functions for super fast detector simulation
namespace CMS
{
/// @name CMS detector efficiency functions
///@{
/// Randomly filter the supplied particle list by parameterised electron tracking efficiency
inline void applyElectronTrackingEff(std::vector<const HEPUtils::Particle*>& electrons)
{
static HEPUtils::BinnedFn2D<double> _elTrackEff2d({{0, 1.5, 2.5, DBL_MAX}}, //< |eta|
{{0, 0.1, 1.0, DBL_MAX}}, //< pT
{{0., 0.70, 0.95, // |eta| 0.1-1.5
0., 0.60, 0.85, // |eta| 1.5-2.5
0., 0., 0.}}); // |eta| > 2.5
filtereff_etapt(electrons, _elTrackEff2d);
}
/// Randomly filter the supplied particle list by parameterised electron efficiency
/// @note Should be applied after the electron energy smearing
/// @note Eff values currently identical to those in ATLAS (AB, 2016-01-24)
inline void applyElectronEff(std::vector<const HEPUtils::Particle*>& electrons)
{
static HEPUtils::BinnedFn2D<double> _elEff2d({{0, 1.5, 2.5, DBL_MAX}}, //< |eta|
{{0, 10., DBL_MAX}}, //< pT
{{0., 0.95, // |eta| 0.1-1.5
0., 0.85, // |eta| 1.5-2.5
0., 0.}}); // |eta| > 2.5
filtereff_etapt(electrons, _elEff2d);
}
/// Randomly filter the supplied particle list by parameterised muon tracking efficiency
/// @note Eff values currently identical to those in ATLAS (AB, 2016-01-24)
inline void applyMuonTrackEff(std::vector<const HEPUtils::Particle*>& muons)
{
static HEPUtils::BinnedFn2D<double> _muTrackEff2d({{0, 1.5, 2.5, DBL_MAX}}, //< |eta|
{{0, 0.1, 1.0, DBL_MAX}}, //< pT
{{0, 0.75, 0.99, // |eta| 0.1-1.5
0, 0.70, 0.98, // |eta| 1.5-2.5
0, 0, 0}}); // |eta| > 2.5
filtereff_etapt(muons, _muTrackEff2d);
}
/// Randomly filter the supplied particle list by parameterised muon efficiency
inline void applyMuonEff(std::vector<const HEPUtils::Particle*>& muons)
{
if(muons.empty()) return;
auto keptMuonsEnd = std::remove_if(muons.begin(), muons.end(),
[](const HEPUtils::Particle* p)
{
bool rm(p->abseta() > 2.4 || p->pT() < 10);
if (!rm)
{
const double eff = 0.95 * (p->pT() <= 1.0e3 ? 1 : exp(0.5 - 5e-4*p->pT()));
rm = !random_bool(eff);
}
return rm;
} );
muons.erase(keptMuonsEnd, muons.end());
}
/// @brief Randomly filter the supplied particle list by parameterised tau efficiency
/// @note No delete, because this should only ever be applied to copies of the Event Particle* vectors in Analysis routines
inline void applyTauEfficiency(std::vector<const HEPUtils::Particle*>& taus)
{
filtereff(taus, 0.6);
}
/// @brief Randomly smear the supplied electrons' momenta by parameterised resolutions
///
/// Function that mimics the DELPHES electron energy resolution.
/// We need to smear E, then recalculate pT, then reset the 4-vector.
inline void smearElectronEnergy(std::vector<HEPUtils::Particle*>& electrons)
{
// Now loop over the electrons and smear the 4-vectors
for (HEPUtils::Particle* e : electrons)
{
// 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)
double resolution = 0;
const double abseta = e->abseta();
if (e->pT() > 0.1 && abseta < 2.5)
{
if (abseta < 0.5)
{
resolution = HEPUtils::add_quad(0.06, 1.3e-3 * e->pT());
}
else if (abseta < 1.5)
{
resolution = HEPUtils::add_quad(0.10, 1.7e-3 * e->pT());
}
else
{ // still |eta| < 2.5
resolution = HEPUtils::add_quad(0.25, 3.1e-3 * e->pT());
}
}
// Smear by a Gaussian centered on the current energy, with width given by the resolution
if (resolution > 0)
{
std::normal_distribution<> d(e->E(), resolution);
double smeared_E = d(Random::rng());
if (smeared_E < e->mass()) smeared_E = 1.01*e->mass();
// double smeared_pt = smeared_E/cosh(e->eta()); ///< @todo Should be cosh(|eta|)?
e->set_mom(HEPUtils::P4::mkEtaPhiME(e->eta(), e->phi(), e->mass(), smeared_E));
}
}
}
/// @brief Randomly smear the supplied muons' momenta by parameterised resolutions
///
/// Function that mimics the DELPHES muon momentum resolution.
/// We need to smear pT, then recalculate E, then reset the 4-vector.
inline void smearMuonMomentum(std::vector<HEPUtils::Particle*>& muons)
{
// Now loop over the muons and smear the 4-vectors
for (HEPUtils::Particle* p : muons)
{
// Calculate 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 = p->abseta();
if (p->pT() > 0.1 && abseta < 2.5)
{
if (abseta < 0.5)
{
resolution = HEPUtils::add_quad(0.01, 2.0e-4 * p->pT());
}
else if (abseta < 1.5)
{
resolution = HEPUtils::add_quad(0.02, 3.0e-4 * p->pT());
}
else
{ // still |eta| < 2.5... but isn't CMS' mu acceptance < 2.4?
resolution = HEPUtils::add_quad(0.05, 2.6e-4 * p->pT());
}
}
// Smear by a Gaussian centered on the current pT, with width given by the resolution
std::normal_distribution<> d(p->pT(), resolution*p->pT());
double smeared_pt = d(Random::rng());
if (smeared_pt < 0) smeared_pt = 0;
// const double smeared_E = smeared_pt*cosh(mu->eta()); ///< @todo Should be cosh(|eta|)?
// std::cout << "Muon pt " << mu_pt << " smeared " << smeared_pt << std::endl;
p->set_mom(HEPUtils::P4::mkEtaPhiMPt(p->eta(), p->phi(), p->mass(), smeared_pt));
}
}
/// @brief Randomly smear the supplied jets' momenta by parameterised resolutions
///
/// Function that mimics the DELPHES jet momentum resolution.
/// We need to smear pT, then recalculate E, then reset the 4-vector
///
/// @todo Update cf. Matthias study for ATLAS
inline void smearJets(std::vector<HEPUtils::Jet*>& jets)
{
// Const resolution for now
/// @todo This is the ATLAS number... I can't find values for the CMS parameterisation, cf.
/// https://cds.cern.ch/record/1339945/files/JME-10-014-pas.pdf
/// https://twiki.cern.ch/twiki/bin/view/CMSPublic/SWGuideJetResolution
/// https://github.com/adrager/cmssw/blob/CMSSW_7_2_X/CondFormats/JetMETObjects/test/TestCorrections.C
//const double resolution = 0.03;
// Matthias jet smearing implemented roughly from https://arxiv.org/pdf/1607.03663.pdf
// Parameterisation can be still improved as functional form is given
// Pileup of <mu>=25 is taken, as JER depends strongly on mu
// CMS does not include information about JER at eta>1.3
const std::vector<double> binedges_eta = {0,10.};
const std::vector<double> binedges_pt = {0,20,30,40,50.,70.,100.,150.,200.,1000.,10000.};
const std::vector<double> JetsJER = {0.3,0.2,0.16,0.145,0.12,0.1,0.09,0.08,0.06,0.05};
static HEPUtils::BinnedFn2D<double> _resJets2D(binedges_eta,binedges_pt,JetsJER);
// Now loop over the jets and smear the 4-vectors
for (HEPUtils::Jet* jet : jets)
{
const double resolution = _resJets2D.get_at(jet->abseta(), jet->pT());
std::normal_distribution<> d(1., resolution);
// Smear by a Gaussian centered on 1 with width given by the (fractional) resolution
double smear_factor = d(Random::rng());
jet->set_mom(HEPUtils::P4::mkXYZM(jet->mom().px()*smear_factor, jet->mom().py()*smear_factor, jet->mom().pz()*smear_factor, jet->mass()));
}
}
/// @brief Randomly smear the supplied hadronic taus' momenta by parameterised resolutions
///
/// We need to smear pT, then recalculate E, then reset the 4-vector.
/// Same as for jets, but on a vector of particles. (?)
///
/// @todo Update cf. Matthias study for ATLAS
inline void smearTaus(std::vector<HEPUtils::Particle*>& taus)
{
// Const resolution for now
const double resolution = 0.03;
// Now loop over the jets and smear the 4-vectors
std::normal_distribution<> d(1., resolution);
for (HEPUtils::Particle* p : taus)
{
// Smear by a Gaussian centered on 1 with width given by the (fractional) resolution
double smear_factor = d(Random::rng());
/// @todo Is this the best way to smear? Should we preserve the mean jet energy, or pT, or direction?
p->set_mom(HEPUtils::P4::mkXYZM(p->mom().px()*smear_factor, p->mom().py()*smear_factor, p->mom().pz()*smear_factor, p->mass()));
}
}
///Apply efficiency function to CSVv2 medium WP b-tagged jets
///@note Numbers digitized from https://twiki.cern.ch/twiki/pub/CMSPublic/SUSMoriond2017ObjectsEfficiency/btag_eff_CSVv2_DeepCSV.pdf
inline void applyCSVv2MediumBtagEff(std::vector<const HEPUtils::Jet*>& bjets)
{
if (bjets.empty()) return;
const static std::vector<double> binedges_et = {25., 40., 60., 80., 100., 150., 200., 250., 300., 400., 500.,DBL_MAX };
const static std::vector<double> bineffs_et = {0.58, 0.61, 0.63, 0.64, 0.65, 0.62,0.6, 0.58, 0.56, 0.52, 0.48};
const static HEPUtils::BinnedFn1D<double> _eff_et(binedges_et, bineffs_et);
auto keptBjetsEnd = std::remove_if(bjets.begin(), bjets.end(),
[](const HEPUtils::Jet* bjet)
{
const double bjet_pt = bjet->pT();
const double bjet_aeta = bjet->abseta();
if (bjet_aeta > 2.4 || bjet_pt < 25) return true;
const double eff = _eff_et.get_at(bjet_pt);
return random_bool(1-eff);
} );
bjets.erase(keptBjetsEnd, bjets.end());
}
inline void applyCSVv2MediumBtagEff(std::vector<HEPUtils::Jet*>& bjets)
{
applyCSVv2MediumBtagEff(reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(bjets));
}
///Apply efficiency function to CSVv2 loose WP b-tagged jets
///@note Numbers digitized from https://twiki.cern.ch/twiki/pub/CMSPublic/SUSMoriond2017ObjectsEfficiency/btag_eff_CSVv2_DeepCSV.pdf
inline void applyCSVv2LooseBtagEff(std::vector<const HEPUtils::Jet*>& bjets)
{
if (bjets.empty()) return;
const static std::vector<double> binedges_et = {25., 40., 60., 80., 100., 150., 200., 250., 300., 400., 500.,DBL_MAX };
const static std::vector<double> bineffs_et = {0.78, 0.80, 0.82, 0.83, 0.84, 0.825, 0.82, 0.81, 0.8, 0.795, 0.79};
const static HEPUtils::BinnedFn1D<double> _eff_et(binedges_et, bineffs_et);
auto keptBjetsEnd = std::remove_if(bjets.begin(), bjets.end(),
[](const HEPUtils::Jet* bjet)
{
const double bjet_pt = bjet->pT();
const double bjet_aeta = bjet->abseta();
if (bjet_aeta > 2.4 || bjet_pt < 25) return true;
const double eff = _eff_et.get_at(bjet_pt);
return random_bool(1-eff);
} );
bjets.erase(keptBjetsEnd, bjets.end());
}
inline void applyCSVv2LooseBtagEff(std::vector<HEPUtils::Jet*>& bjets)
{
applyCSVv2LooseBtagEff(reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(bjets));
}
///Apply efficiency function to CSVv2 tight WP b-tagged jets
///@note Numbers digitized from https://twiki.cern.ch/twiki/pub/CMSPublic/SUSMoriond2017ObjectsEfficiency/btag_eff_CSVv2_DeepCSV.pdf
inline void applyCSVv2TightBtagEff(std::vector<const HEPUtils::Jet*>& bjets)
{
if (bjets.empty()) return;
const static std::vector<double> binedges_et = {25., 40., 60., 80., 100., 150., 200., 250., 300., 400., 500.,DBL_MAX };
const static std::vector<double> bineffs_et = {0.4997, 0.5081, 0.5104, 0.5085, 0.4994, 0.4790, 0.4481, 0.4184, 0.3798, 0.3394, 0.3};
const static HEPUtils::BinnedFn1D<double> _eff_et(binedges_et, bineffs_et);
auto keptBjetsEnd = std::remove_if(bjets.begin(), bjets.end(),
[](const HEPUtils::Jet* bjet)
{
const double bjet_pt = bjet->pT();
const double bjet_aeta = bjet->abseta();
if (bjet_aeta > 2.4 || bjet_pt < 25) return true;
const double eff = _eff_et.get_at(bjet_pt);
return random_bool(1-eff);
} );
bjets.erase(keptBjetsEnd, bjets.end());
}
inline void applyCSVv2TightBtagEff(std::vector<HEPUtils::Jet*>& bjets)
{
applyCSVv2TightBtagEff(reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(bjets));
}
///Apply user-specified b-tag misidentification rate (flat)
inline void applyBtagMisId(double mis_id_prob, std::vector<const HEPUtils::Jet*>& jets, std::vector<const HEPUtils::Jet*>& bjets)
{
if (jets.empty()) return;
for (const HEPUtils::Jet* jet : jets)
{
// Only apply misidentification rate for non-b-jets
if (!jet->btag() && random_bool(mis_id_prob)) bjets.push_back(jet);
}
}
inline void applyBtagMisId(double mis_id_prob, std::vector<HEPUtils::Jet*>& jets, std::vector<HEPUtils::Jet*>& bjets)
{
applyBtagMisId(mis_id_prob, reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(jets), reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(bjets));
}
///Apply b-tag misidentification rate for CSVv2 loose WP
///@note Numbers from Table 2 in https://arxiv.org/pdf/1712.07158.pdf
inline void applyCSVv2LooseBtagMisId(std::vector<const HEPUtils::Jet*>& jets, std::vector<const HEPUtils::Jet*>& bjets)
{
if (jets.empty()) return;
// For now we apply the (pT-averaged) light-flavour misidentification rate to all jets.
// Realistically, the rate should be higher for c-jets.
const static double mis_id_prob = 0.089;
applyBtagMisId(mis_id_prob, jets, bjets);
}
inline void applyCSVv2LooseBtagMisId(std::vector<HEPUtils::Jet*>& jets, std::vector<HEPUtils::Jet*>& bjets)
{
applyCSVv2LooseBtagMisId(reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(jets), reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(bjets));
}
///Apply both b-tag efficiency and misidentification rate for CSVv2 loose WP
inline void applyCSVv2LooseBtagEffAndMisId(std::vector<const HEPUtils::Jet*>& jets, std::vector<const HEPUtils::Jet*>& bjets)
{
if (jets.empty() && bjets.empty()) return;
// Apply b-tag efficiency
applyCSVv2LooseBtagEff(bjets);
// Apply misidentification rate to the non-b-jets in the jets vector
applyCSVv2LooseBtagMisId(jets, bjets);
}
inline void applyCSVv2LooseBtagEffAndMisId(std::vector<HEPUtils::Jet*>& jets, std::vector<HEPUtils::Jet*>& bjets)
{
applyCSVv2LooseBtagEffAndMisId(reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(jets), reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(bjets));
}
///Apply b-tag misidentification rate for CSVv2 medium WP
///@note Numbers from Table 2 in https://arxiv.org/pdf/1712.07158.pdf
inline void applyCSVv2MediumBtagMisId(std::vector<const HEPUtils::Jet*>& jets, std::vector<const HEPUtils::Jet*>& bjets)
{
if (jets.empty()) return;
// For now we apply the (pT-averaged) light-flavour misidentification rate to all jets.
// Realistically, the rate should be higher for c-jets.
const static double mis_id_prob = 0.009;
applyBtagMisId(mis_id_prob, jets, bjets);
}
inline void applyCSVv2MediumBtagMisId(std::vector<HEPUtils::Jet*>& jets, std::vector<HEPUtils::Jet*>& bjets)
{
applyCSVv2MediumBtagMisId(reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(jets), reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(bjets));
}
///Apply both b-tag efficiency and misidentification rate for CSVv2 medium WP
inline void applyCSVv2MediumBtagEffAndMisId(std::vector<const HEPUtils::Jet*>& jets, std::vector<const HEPUtils::Jet*>& bjets)
{
if (jets.empty() && bjets.empty()) return;
// Apply b-tag efficiency
applyCSVv2MediumBtagEff(bjets);
// Apply misidentification rate to the non-b-jets in the jets vector
applyCSVv2MediumBtagMisId(jets, bjets);
}
inline void applyCSVv2MediumBtagEffAndMisId(std::vector<HEPUtils::Jet*>& jets, std::vector<HEPUtils::Jet*>& bjets)
{
applyCSVv2MediumBtagEffAndMisId(reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(jets), reinterpret_cast<std::vector<const HEPUtils::Jet*>&>(bjets));
}
///@}
/// Representative Muon and Electron efficiencies for the WPs of the identification techniques used in SUSY analyses
/// From https://twiki.cern.ch/twiki/bin/view/CMSPublic/SUSMoriond2017ObjectsEfficiency
///{@
// Efficiencies from the 2016 Multilepton EWK analyses (SUS_16_039)
// Electrons
// Numbers digitized from https://twiki.cern.ch/twiki/pub/CMSPublic/SUSMoriond2017ObjectsEfficiency/2d_full_pteta_el_039_multi_ttbar.pdf
// The efficiency map has been extended to cover the low-pT region, using the efficiencies from BuckFast (CMSEfficiencies.hpp)
static const HEPUtils::BinnedFn2D<double> eff2DEl_SUS_16_039(
{0., 0.8, 1.442, 1.556, 2., 2.5, DBL_MAX}, // Bin edges in eta
{0., 10., 15., 20., 25., 30., 40., 50., DBL_MAX}, // Bin edges in pT. Assume flat efficiency above 200, where the CMS map stops.
{
// pT: (0,10), (10,15), (15,20), (20,25), (25,30), (30,40), (40,50), (50,inf)
0.0, 0.95, 0.507, 0.619, 0.682, 0.742, 0.798, 0.863, // eta: (0, 0.8)
0.0, 0.95, 0.429, 0.546, 0.619, 0.710, 0.734, 0.833, // eta: (0.8, 1.4429
0.0, 0.95, 0.256, 0.221, 0.315, 0.351, 0.373, 0.437, // eta: (1.442, 1.556)
0.0, 0.85, 0.249, 0.404, 0.423, 0.561, 0.642, 0.749, // eta: (1.556, 2)
0.0, 0.85, 0.195, 0.245, 0.380, 0.441, 0.533, 0.644, // eta: (2, 2.5)
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 // eta > 2.5
}
);
// Muons
// Numbers digitized from https://twiki.cern.ch/twiki/pub/CMSPublic/SUSMoriond2017ObjectsEfficiency/2d_full_pteta_mu_039_multi_ttbar.pdf
// The efficiency map has been extended to cover the low-pT region, using the efficiencies from BuckFast (CMSEfficiencies.hpp)
static const HEPUtils::BinnedFn2D<double> eff2DMu_SUS_16_039(
{0., 0.9, 1.2, 2.1, 2.4, DBL_MAX}, // Bin edges in eta
{0., 10., 15., 20., 25., 30., 40., 50., DBL_MAX}, // Bin edges in pT. Assume flat efficiency above 200, where the CMS map stops.
{
// pT: (0,10), (10,15), (15,20), (20,25), (25,30), (30,40), (40,50), (50,inf)
0.0, 0.704, 0.797, 0.855, 0.880, 0.906, 0.927, 0.931, // eta: (0, 0.9)
0.0, 0.639, 0.776, 0.836, 0.875, 0.898, 0.940, 0.930, // eta: (0.9, 1.2)
0.0, 0.596, 0.715, 0.840, 0.862, 0.891, 0.906, 0.925, // eta: (1.2, 2.1)
0.0, 0.522, 0.720, 0.764, 0.803, 0.807, 0.885, 0.877, // eta: (2.1, 2.4)
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 // eta > 2.4
}
);
// Taus (Tight WP)
// Numbers digitized from https://twiki.cern.ch/twiki/pub/CMSPublic/SUSMoriond2017ObjectsEfficiency/TauIDEfficiency_pT_DP2016_066.pdf
// The tau efficiencies should be corrected with a data/simulation scale factor of 0.95, as instructed here: https://twiki.cern.ch/twiki/bin/view/CMSPublic/SUSMoriond2017ObjectsEfficiency
static const HEPUtils::BinnedFn2D<double> eff2DTau_SUS_16_039(
{0.,2.3},
{0.,25.,30.,35.,40.,45.,50.,60.,70.,80.,DBL_MAX}, // Assuming flat efficiency above pT = 100 GeV, where the CMS map stops.
{0.38*0.95, 0.48*0.95, 0.5*0.95, 0.49*0.95, 0.51*0.95, 0.49*0.95, 0.47*0.95, 0.45*0.95, 0.48*0.95, 0.5*0.95}
);
// Efficiencies from the 2019 Multilepton EWK analyses (SUS_19_008)
// Electrons
// Numbers digitized from https://twiki.cern.ch/twiki/pub/CMSPublic/SUSMoriond2017ObjectsEfficiency/eff_el.pdf
// The efficiency map has been extended to cover the low-pT region with the efficiencies from the 2016 data above
static const HEPUtils::BinnedFn2D<double> eff2DEl_SUS_19_008(
{0., 0.8, 1.442, 1.556, 2., 2.5, DBL_MAX}, // Bin edges in eta
{0., 10., 15., 20., 25., 30., 40., 50., DBL_MAX}, // Bin edges in pT. Assume flat efficiency above 200, where the CMS map stops.
{
// pT: (0,10), (10,15), (15,20), (20,25), (25,30), (30,40), (40,50), (50,inf)
0.0, 0.95, 0.330, 0.412, 0.487, 0.561, 0.615, 0.701, // eta: (0, 0.8)
0.0, 0.95, 0.276, 0.367, 0.434, 0.520, 0.575, 0.660, // eta: (0.8, 1.4429
0.0, 0.95, 0.202, 0.170, 0.224, 0.261, 0.275, 0.341, // eta: (1.442, 1.556)
0.0, 0.85, 0.210, 0.288, 0.358, 0.434, 0.493, 0.586, // eta: (1.556, 2)
0.0, 0.85, 0.146, 0.200, 0.246, 0.314, 0.382, 0.456, // eta: (2, 2.5)
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 // eta > 2.5
}
);
// Muons
// Numbers digitized from https://twiki.cern.ch/twiki/pub/CMSPublic/SUSMoriond2017ObjectsEfficiency/eff_mu.pdf
// The efficiency map has been extended to cover the low-pT region with the efficiencies from the 2016 data above
static const HEPUtils::BinnedFn2D<double> eff2DMu_SUS_19_008(
{0., 0.9, 1.2, 2.1, 2.4, DBL_MAX}, // Bin edges in eta
{0., 10., 15., 20., 25., 30., 40., 50., DBL_MAX}, // Bin edges in pT. Assume flat efficiency above 200, where the CMS map stops.
{
// pT: (0,10), (10,15), (15,20), (20,25), (25,30), (30,40), (40,50), (50,inf)
0.0, 0.527, 0.639, 0.723, 0.801, 0.858, 0.887, 0.926, // eta: (0, 0.9)
0.0, 0.482, 0.596, 0.695, 0.755, 0.831, 0.870, 0.917, // eta: (0.9, 1.2)
0.0, 0.498, 0.585, 0.683, 0.743, 0.807, 0.851, 0.896, // eta: (1.2, 2.1)
0.0, 0.441, 0.522, 0.604, 0.677, 0.744, 0.793, 0.834, // eta: (2.1, 2.4)
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 // eta > 2.4
}
);
// Map of electron efficiencies
static const std::map<str, HEPUtils::BinnedFn2D<double> > eff2DEl =
{
{"SUS_16_039", eff2DEl_SUS_16_039},
{"SUS_19_008", eff2DEl_SUS_19_008}
};
// Map of muon efficiencies
static const std::map<str, HEPUtils::BinnedFn2D<double> > eff2DMu =
{
{"SUS_16_039", eff2DMu_SUS_16_039},
{"SUS_19_008", eff2DMu_SUS_19_008}
};
// Map of tau efficiencies
static const std::map<str, HEPUtils::BinnedFn2D<double> > eff2DTau =
{
{"SUS_16_039", eff2DTau_SUS_16_039}
};
///@}
}
}
}
Updated on 2024-07-18 at 13:53:34 +0000