ATLAS_2012_I1204447 Class Reference
Detailed DescriptionDefinition at line 14 of file ATLAS_2012_I1204447.cc. Constructor & Destructor Documentation
Constructor. Definition at line 18 of file ATLAS_2012_I1204447.cc. : Analysis("ATLAS_2012_I1204447") { } Member Function Documentation
Non-templated version of string-based applyProjection, to work around header dependency issue. Definition at line 22 of file ProjectionApplier.cc. {
return evt.applyProjection(getProjection(name));
}
Non-templated version of proj-based applyProjection, to work around header dependency issue. Definition at line 28 of file ProjectionApplier.cc. {
return evt.applyProjection(proj);
}
Untemplated function to do the work... Definition at line 34 of file ProjectionApplier.cc. {
if (!_allowProjReg) {
cerr << "Trying to register projection '"
<< proj.name() << "' before init phase in '" << this->name() << "'." << endl;
exit(2);
}
const Projection& reg = getProjHandler().registerProjection(*this, proj, name);
return reg;
}
Register a data object in the histogram system.
Definition at line 795 of file Analysis.cc. {
_analysisobjects.push_back(ao);
}
Register a contained projection (user-facing version)
Definition at line 157 of file ProjectionApplier.hh. { return declareProjection(proj, name); }
List of registered analysis data objects. Definition at line 811 of file Analysis.hh. {
return _analysisobjects;
}
Perform the per-event analysis.
Implements Analysis. Definition at line 88 of file ATLAS_2012_I1204447.cc. {
// Muons
Particles muon_candidates;
const Particles charged_tracks = apply<ChargedFinalState>(event, "CFS").particles();
const Particles visible_particles = apply<VisibleFinalState>(event, "VFS").particles();
foreach (const Particle& mu, apply<IdentifiedFinalState>(event, "muons").particlesByPt()) {
// Calculate pTCone30 variable (pT of all tracks within dR<0.3 - pT of muon itself)
double pTinCone = -mu.pT();
foreach (const Particle& track, charged_tracks) {
if (deltaR(mu.momentum(), track.momentum()) < 0.3)
pTinCone += track.pT();
}
// Calculate eTCone30 variable (pT of all visible particles within dR<0.3)
double eTinCone = 0.;
foreach (const Particle& visible_particle, visible_particles) {
if (visible_particle.abspid() != PID::MUON && inRange(deltaR(mu.momentum(), visible_particle.momentum()), 0.1, 0.3))
eTinCone += visible_particle.pT();
}
// Apply reconstruction efficiency and simulate reco
int muon_id = 13;
if ( mu.hasAncestor(15) || mu.hasAncestor(-15)) muon_id = 14;
const double eff = (_use_fiducial_lepton_efficiency) ? apply_reco_eff(muon_id, mu) : 1.0;
const bool keep_muon = rand()/static_cast<double>(RAND_MAX) <= eff;
// Keep muon if pTCone30/pT < 0.15 and eTCone30/pT < 0.2 and reconstructed
if (keep_muon && pTinCone/mu.pT() <= 0.15 && eTinCone/mu.pT() < 0.2)
muon_candidates.push_back(mu);
}
// Electrons
Particles electron_candidates;
foreach (const Particle& e, apply<IdentifiedFinalState>(event, "elecs").particlesByPt()) {
// Neglect electrons in crack regions
if (inRange(e.abseta(), 1.37, 1.52)) continue;
// Calculate pTCone30 variable (pT of all tracks within dR<0.3 - pT of electron itself)
double pTinCone = -e.pT();
foreach (const Particle& track, charged_tracks) {
if (deltaR(e.momentum(), track.momentum()) < 0.3) pTinCone += track.pT();
}
// Calculate eTCone30 variable (pT of all visible particles (except muons) within dR<0.3)
double eTinCone = 0.;
foreach (const Particle& visible_particle, visible_particles) {
if (visible_particle.abspid() != PID::MUON && inRange(deltaR(e.momentum(), visible_particle.momentum()), 0.1, 0.3))
eTinCone += visible_particle.pT();
}
// Apply reconstruction efficiency and simulate reco
int elec_id = 11;
if (e.hasAncestor(15) || e.hasAncestor(-15)) elec_id = 12;
const double eff = (_use_fiducial_lepton_efficiency) ? apply_reco_eff(elec_id, e) : 1.0;
const bool keep_elec = rand()/static_cast<double>(RAND_MAX) <= eff;
// Keep electron if pTCone30/pT < 0.13 and eTCone30/pT < 0.2 and reconstructed
if (keep_elec && pTinCone/e.pT() <= 0.13 && eTinCone/e.pT() < 0.2)
electron_candidates.push_back(e);
}
// Taus
/// @todo This could benefit from a tau finder projection
Particles tau_candidates;
foreach (const Particle& tau, apply<UnstableFinalState>(event, "UFS").particlesByPt()) {
// Only pick taus out of all unstable particles
if (tau.abspid() != PID::TAU) continue;
// Check that tau has decayed into daughter particles
/// @todo Huh? Unstable taus with no decay vtx? Can use Particle.isStable()? But why in this situation?
if (tau.genParticle()->end_vertex() == 0) continue;
// Calculate visible tau pT from pT of tau neutrino in tau decay for pT and |eta| cuts
FourMomentum daughter_tau_neutrino_momentum = get_tau_neutrino_mom(tau);
Particle tau_vis = tau;
tau_vis.setMomentum(tau.momentum()-daughter_tau_neutrino_momentum);
// keep only taus in certain eta region and above 15 GeV of visible tau pT
if ( tau_vis.pT() <= 15.0*GeV || tau_vis.abseta() > 2.5) continue;
// Get prong number (number of tracks) in tau decay and check if tau decays leptonically
unsigned int nprong = 0;
bool lep_decaying_tau = false;
get_prong_number(tau.genParticle(), nprong, lep_decaying_tau);
// Apply reconstruction efficiency
int tau_id = 15;
if (nprong == 1) tau_id = 15;
else if (nprong == 3) tau_id = 16;
// Get fiducial lepton efficiency simulate reco efficiency
const double eff = (_use_fiducial_lepton_efficiency) ? apply_reco_eff(tau_id, tau_vis) : 1.0;
const bool keep_tau = rand()/static_cast<double>(RAND_MAX) <= eff;
// Keep tau if nprong = 1, it decays hadronically, and it's reconstructed by the detector
if ( !lep_decaying_tau && nprong == 1 && keep_tau) tau_candidates.push_back(tau_vis);
}
// Jets (all anti-kt R=0.4 jets with pT > 25 GeV and eta < 4.9)
Jets jet_candidates;
foreach (const Jet& jet, apply<FastJets>(event, "AntiKtJets04").jetsByPt(25*GeV)) {
if (jet.abseta() < 4.9) jet_candidates.push_back(jet);
}
// ETmiss
Particles vfs_particles = apply<VisibleFinalState>(event, "VFS").particles();
FourMomentum pTmiss;
foreach (const Particle& p, vfs_particles) pTmiss -= p.momentum();
double eTmiss = pTmiss.pT()/GeV;
//------------------
// Overlap removal
// electron - electron
Particles electron_candidates_2;
for (size_t ie = 0; ie < electron_candidates.size(); ++ie) {
const Particle & e = electron_candidates[ie];
bool away = true;
// If electron pair within dR < 0.1: remove electron with lower pT
for (size_t ie2=0; ie2 < electron_candidates_2.size(); ++ie2) {
if ( deltaR( e.momentum(), electron_candidates_2[ie2].momentum()) < 0.1 ) {
away = false;
break;
}
}
// If isolated keep it
if ( away )
electron_candidates_2.push_back( e );
}
// jet - electron
Jets recon_jets;
foreach (const Jet& jet, jet_candidates) {
bool away = true;
// if jet within dR < 0.2 of electron: remove jet
foreach (const Particle& e, electron_candidates_2) {
if (deltaR(e.momentum(), jet.momentum()) < 0.2) {
away = false;
break;
}
}
// jet - tau
if (away) {
// If jet within dR < 0.2 of tau: remove jet
foreach (const Particle& tau, tau_candidates) {
if (deltaR(tau.momentum(), jet.momentum()) < 0.2) {
away = false;
break;
}
}
}
// If isolated keep it
if ( away )
recon_jets.push_back( jet );
}
// electron - jet
Particles recon_leptons, recon_e;
for (size_t ie = 0; ie < electron_candidates_2.size(); ++ie) {
const Particle& e = electron_candidates_2[ie];
// If electron within 0.2 < dR < 0.4 from any jets: remove electron
bool away = true;
foreach (const Jet& jet, recon_jets) {
if (deltaR(e.momentum(), jet.momentum()) < 0.4) {
away = false;
break;
}
}
// electron - muon
// if electron within dR < 0.1 of a muon: remove electron
if (away) {
foreach (const Particle& mu, muon_candidates) {
if (deltaR(mu.momentum(), e.momentum()) < 0.1) {
away = false;
break;
}
}
}
// If isolated keep it
if (away) {
recon_e += e;
recon_leptons += e;
}
}
// tau - electron
Particles recon_tau;
foreach ( const Particle& tau, tau_candidates ) {
bool away = true;
// If tau within dR < 0.2 of an electron: remove tau
foreach ( const Particle& e, recon_e ) {
if (deltaR( tau.momentum(), e.momentum()) < 0.2) {
away = false;
break;
}
}
// tau - muon
// If tau within dR < 0.2 of a muon: remove tau
if (away) {
foreach (const Particle& mu, muon_candidates) {
if (deltaR(tau.momentum(), mu.momentum()) < 0.2) {
away = false;
break;
}
}
}
// If isolated keep it
if (away) recon_tau.push_back( tau );
}
// Muon - jet isolation
Particles recon_mu, trigger_mu;
// If muon within dR < 0.4 of a jet, remove muon
foreach (const Particle& mu, muon_candidates) {
bool away = true;
foreach (const Jet& jet, recon_jets) {
if ( deltaR( mu.momentum(), jet.momentum()) < 0.4 ) {
away = false;
break;
}
}
if (away) {
recon_mu.push_back( mu );
recon_leptons.push_back( mu );
if (mu.abseta() < 2.4) trigger_mu.push_back( mu );
}
}
// End overlap removal
//------------------
// Jet cleaning
if (rand()/static_cast<double>(RAND_MAX) <= 0.42) {
foreach (const Jet& jet, recon_jets) {
const double eta = jet.rapidity();
const double phi = jet.azimuthalAngle(MINUSPI_PLUSPI);
if (jet.pT() > 25*GeV && inRange(eta, -0.1, 1.5) && inRange(phi, -0.9, -0.5)) vetoEvent;
}
}
// Post-isolation event cuts
// Require at least 3 charged tracks in event
if (charged_tracks.size() < 3) vetoEvent;
// And at least one e/mu passing trigger
if (!( !recon_e .empty() && recon_e[0] .pT() > 25*GeV) &&
!( !trigger_mu.empty() && trigger_mu[0].pT() > 25*GeV) ) {
MSG_DEBUG("Hardest lepton fails trigger");
vetoEvent;
}
// And only accept events with at least 2 electrons and muons and at least 3 leptons in total
if (recon_mu.size() + recon_e.size() + recon_tau.size() < 3 || recon_leptons.size() < 2) vetoEvent;
// Now it's worth getting the event weight
const double weight = event.weight();
// Sort leptons by decreasing pT
sortByPt(recon_leptons);
sortByPt(recon_tau);
// Calculate HTlep, fill lepton pT histograms & store chosen combination of 3 leptons
double HTlep = 0.;
Particles chosen_leptons;
if ( recon_leptons.size() > 2 ) {
_h_pt_1_3l->fill(recon_leptons[0].perp()/GeV, weight);
_h_pt_2_3l->fill(recon_leptons[1].perp()/GeV, weight);
_h_pt_3_3l->fill(recon_leptons[2].perp()/GeV, weight);
HTlep = (recon_leptons[0].pT() + recon_leptons[1].pT() + recon_leptons[2].pT())/GeV;
chosen_leptons.push_back( recon_leptons[0] );
chosen_leptons.push_back( recon_leptons[1] );
chosen_leptons.push_back( recon_leptons[2] );
}
else {
_h_pt_1_2ltau->fill(recon_leptons[0].perp()/GeV, weight);
_h_pt_2_2ltau->fill(recon_leptons[1].perp()/GeV, weight);
_h_pt_3_2ltau->fill(recon_tau[0].perp()/GeV, weight);
HTlep = (recon_leptons[0].pT() + recon_leptons[1].pT() + recon_tau[0].pT())/GeV ;
chosen_leptons.push_back( recon_leptons[0] );
chosen_leptons.push_back( recon_leptons[1] );
chosen_leptons.push_back( recon_tau[0] );
}
// Number of prompt e/mu and had taus
_h_e_n ->fill(recon_e.size() , weight);
_h_mu_n ->fill(recon_mu.size() , weight);
_h_tau_n->fill(recon_tau.size(), weight);
// Calculate HTjets
double HTjets = 0.;
foreach ( const Jet & jet, recon_jets )
HTjets += jet.perp()/GeV;
// Calculate meff
double meff = eTmiss + HTjets;
Particles all_leptons;
foreach ( const Particle & e , recon_e ) {
meff += e.perp()/GeV;
all_leptons.push_back( e );
}
foreach ( const Particle & mu, recon_mu ) {
meff += mu.perp()/GeV;
all_leptons.push_back( mu );
}
foreach ( const Particle & tau, recon_tau ) {
meff += tau.perp()/GeV;
all_leptons.push_back( tau );
}
// Fill histogram of kinematic variables
_h_HTlep_all ->fill(HTlep , weight);
_h_HTjets_all->fill(HTjets, weight);
_h_MET_all ->fill(eTmiss, weight);
_h_Meff_all ->fill(meff , weight);
// Determine signal region (3l/2ltau, onZ/offZ)
string basic_signal_region;
if ( recon_mu.size() + recon_e.size() > 2 )
basic_signal_region += "3l_";
else if ( (recon_mu.size() + recon_e.size() == 2) && (recon_tau.size() > 0))
basic_signal_region += "2ltau_";
// Is there an OSSF pair or a three lepton combination with an invariant mass close to the Z mass
int onZ = isonZ(chosen_leptons);
if (onZ == 1) basic_signal_region += "onZ";
else if (onZ == 0) basic_signal_region += "offZ";
// Check in which signal regions this event falls and adjust event counters
fillEventCountsPerSR(basic_signal_region, onZ, HTlep, eTmiss, HTjets, meff, weight);
}
Apply the supplied projection on event evt (user-facing alias).
Definition at line 80 of file ProjectionApplier.hh. { return applyProjection<PROJ>(evt, proj); }
Apply the supplied projection on event evt (user-facing alias).
Definition at line 92 of file ProjectionApplier.hh. { return applyProjection<PROJ>(evt, proj); }
Apply the supplied projection on event evt (user-facing alias).
Definition at line 104 of file ProjectionApplier.hh. { return applyProjection<PROJ>(evt, name); }
Function giving fiducial lepton efficiency. Definition at line 661 of file ATLAS_2012_I1204447.cc. {
float pt = p.pT()/GeV;
float eta = p.eta();
double eff = 0.;
//double err = 0.;
if (flavor == 11) { // weight prompt electron -- now including data/MC ID SF in eff.
//float rho = 0.820;
float p0 = 7.34; float p1 = 0.8977;
//float ep0= 0.5 ; float ep1= 0.0087;
eff = p1 - p0/pt;
//double err0 = ep0/pt; // d(eff)/dp0
//double err1 = ep1; // d(eff)/dp1
//err = sqrt(err0*err0 + err1*err1 - 2*rho*err0*err1);
double avgrate = 0.6867;
float wz_ele_eta[] = {0.588717,0.603674,0.666135,0.747493,0.762202,0.675051,0.751606,0.745569,0.665333,0.610432,0.592693,};
//float ewz_ele_eta[] ={0.00292902,0.002476,0.00241209,0.00182319,0.00194339,0.00299785,0.00197339,0.00182004,0.00241793,0.00245997,0.00290394,};
int ibin = 3;
if (eta >= -2.5 && eta < -2.0) ibin = 0;
if (eta >= -2.0 && eta < -1.5) ibin = 1;
if (eta >= -1.5 && eta < -1.0) ibin = 2;
if (eta >= -1.0 && eta < -0.5) ibin = 3;
if (eta >= -0.5 && eta < -0.1) ibin = 4;
if (eta >= -0.1 && eta < 0.1) ibin = 5;
if (eta >= 0.1 && eta < 0.5) ibin = 6;
if (eta >= 0.5 && eta < 1.0) ibin = 7;
if (eta >= 1.0 && eta < 1.5) ibin = 8;
if (eta >= 1.5 && eta < 2.0) ibin = 9;
if (eta >= 2.0 && eta < 2.5) ibin = 10;
double eff_eta = wz_ele_eta[ibin];
//double err_eta = ewz_ele_eta[ibin];
eff = (eff*eff_eta)/avgrate;
}
if (flavor == 12) { // weight electron from tau
//float rho = 0.884;
float p0 = 6.799; float p1 = 0.842;
//float ep0= 0.664; float ep1= 0.016;
eff = p1 - p0/pt;
//double err0 = ep0/pt; // d(eff)/dp0
//double err1 = ep1; // d(eff)/dp1
//err = sqrt(err0*err0 + err1*err1 - 2*rho*err0*err1);
double avgrate = 0.5319;
float wz_elet_eta[] = {0.468945,0.465953,0.489545,0.58709,0.59669,0.515829,0.59284,0.575828,0.498181,0.463536,0.481738,};
//float ewz_elet_eta[] ={0.00933795,0.00780868,0.00792679,0.00642083,0.00692652,0.0101568,0.00698452,0.00643524,0.0080002,0.00776238,0.0094699,};
int ibin = 3;
if (eta >= -2.5 && eta < -2.0) ibin = 0;
if (eta >= -2.0 && eta < -1.5) ibin = 1;
if (eta >= -1.5 && eta < -1.0) ibin = 2;
if (eta >= -1.0 && eta < -0.5) ibin = 3;
if (eta >= -0.5 && eta < -0.1) ibin = 4;
if (eta >= -0.1 && eta < 0.1) ibin = 5;
if (eta >= 0.1 && eta < 0.5) ibin = 6;
if (eta >= 0.5 && eta < 1.0) ibin = 7;
if (eta >= 1.0 && eta < 1.5) ibin = 8;
if (eta >= 1.5 && eta < 2.0) ibin = 9;
if (eta >= 2.0 && eta < 2.5) ibin = 10;
double eff_eta = wz_elet_eta[ibin];
//double err_eta = ewz_elet_eta[ibin];
eff = (eff*eff_eta)/avgrate;
}
if (flavor == 13) {// weight prompt muon
//if eta>0.1
float p0 = -18.21; float p1 = 14.83; float p2 = 0.9312;
//float ep0= 5.06; float ep1= 1.9; float ep2=0.00069;
if ( fabs(eta) < 0.1) {
p0 = 7.459; p1 = 2.615; p2 = 0.5138;
//ep0 = 10.4; ep1 = 4.934; ep2 = 0.0034;
}
double arg = ( pt-p0 )/( 2.*p1 ) ;
eff = 0.5 * p2 * (1.+erf(arg));
//err = 0.1*eff;
}
if (flavor == 14) {// weight muon from tau
if (fabs(eta) < 0.1) {
float p0 = -1.756; float p1 = 12.38; float p2 = 0.4441;
//float ep0= 10.39; float ep1= 7.9; float ep2=0.022;
double arg = ( pt-p0 )/( 2.*p1 ) ;
eff = 0.5 * p2 * (1.+erf(arg));
//err = 0.1*eff;
}
else {
float p0 = 2.102; float p1 = 0.8293;
//float ep0= 0.271; float ep1= 0.0083;
eff = p1 - p0/pt;
//double err0 = ep0/pt; // d(eff)/dp0
//double err1 = ep1; // d(eff)/dp1
//err = sqrt(err0*err0 + err1*err1 - 2*rho*err0*err1);
}
}
if (flavor == 15) {// weight hadronic tau 1p
float wz_tau1p[] = {0.0249278,0.146978,0.225049,0.229212,0.21519,0.206152,0.201559,0.197917,0.209249,0.228336,0.193548,};
//float ewz_tau1p[] ={0.00178577,0.00425252,0.00535052,0.00592126,0.00484684,0.00612941,0.00792099,0.0083006,0.0138307,0.015568,0.0501751,};
int ibin = 0;
if (pt > 15) ibin = 1;
if (pt > 20) ibin = 2;
if (pt > 25) ibin = 3;
if (pt > 30) ibin = 4;
if (pt > 40) ibin = 5;
if (pt > 50) ibin = 6;
if (pt > 60) ibin = 7;
if (pt > 80) ibin = 8;
if (pt > 100) ibin = 9;
if (pt > 200) ibin = 10;
eff = wz_tau1p[ibin];
//err = ewz_tau1p[ibin];
double avgrate = 0.1718;
float wz_tau1p_eta[] = {0.162132,0.176393,0.139619,0.178813,0.185144,0.210027,0.203937,0.178688,0.137034,0.164216,0.163713,};
//float ewz_tau1p_eta[] ={0.00706705,0.00617989,0.00506798,0.00525172,0.00581865,0.00865675,0.00599245,0.00529877,0.00506368,0.00617025,0.00726219,};
ibin = 3;
if (eta >= -2.5 && eta < -2.0) ibin = 0;
if (eta >= -2.0 && eta < -1.5) ibin = 1;
if (eta >= -1.5 && eta < -1.0) ibin = 2;
if (eta >= -1.0 && eta < -0.5) ibin = 3;
if (eta >= -0.5 && eta < -0.1) ibin = 4;
if (eta >= -0.1 && eta < 0.1) ibin = 5;
if (eta >= 0.1 && eta < 0.5) ibin = 6;
if (eta >= 0.5 && eta < 1.0) ibin = 7;
if (eta >= 1.0 && eta < 1.5) ibin = 8;
if (eta >= 1.5 && eta < 2.0) ibin = 9;
if (eta >= 2.0 && eta < 2.5) ibin = 10;
double eff_eta = wz_tau1p_eta[ibin];
//double err_eta = ewz_tau1p_eta[ibin];
eff = (eff*eff_eta)/avgrate;
}
if (flavor == 16) { //weight hadronic tau 3p
float wz_tau3p[] = {0.000587199,0.00247181,0.0013031,0.00280112,};
//float ewz_tau3p[] ={0.000415091,0.000617187,0.000582385,0.00197792,};
int ibin = 0;
if (pt > 15) ibin = 1;
if (pt > 20) ibin = 2;
if (pt > 40) ibin = 3;
if (pt > 80) ibin = 4;
eff = wz_tau3p[ibin];
//err = ewz_tau3p[ibin];
}
return eff;
}
Apply the supplied projection on event evt.
Definition at line 74 of file ProjectionApplier.hh. {
return pcast<PROJ>(_applyProjection(evt, proj));
}
Apply the supplied projection on event evt.
Definition at line 86 of file ProjectionApplier.hh. {
return pcast<PROJ>(_applyProjection(evt, proj));
}
Apply the named projection on event evt.
Definition at line 98 of file ProjectionApplier.hh. {
return pcast<PROJ>(_applyProjection(evt, name));
}
Helper for histogram asymmetry calculation.
Definition at line 674 of file Analysis.cc. {
const string path = s->path();
*s = YODA::asymm(*h1, *h2);
s->setPath(path);
}
Helper for histogram asymmetry calculation.
Names & emails of paper/analysis authors. Names and email of authors in 'NAME <EMAIL>' format. The first name in the list should be the primary contact person. Definition at line 133 of file Analysis.hh. Incoming beam IDs for this run. Definition at line 33 of file Analysis.cc.
Incoming beams for this run. Definition at line 29 of file Analysis.cc.
BibTeX citation key for this article. Definition at line 186 of file Analysis.hh.
BibTeX citation entry for this article. Definition at line 191 of file Analysis.hh.
Book a counter. Definition at line 196 of file Analysis.cc. {
// const string& xtitle,
// const string& ytitle) {
const string path = histoPath(cname);
CounterPtr ctr = make_shared<Counter>(path, title);
addAnalysisObject(ctr);
MSG_TRACE("Made counter " << cname << " for " << name());
// hist->setAnnotation("XLabel", xtitle);
// hist->setAnnotation("YLabel", ytitle);
return ctr;
}
Book a counter, using a path generated from the dataset and axis ID codes The paper, dataset and x/y-axis IDs will be used to build the histo name in the HepData standard way. Definition at line 210 of file Analysis.cc. {
// const string& xtitle,
// const string& ytitle) {
const string axisCode = makeAxisCode(datasetId, xAxisId, yAxisId);
return bookCounter(axisCode, title);
}
Book a 1D histogram with nbins uniformly distributed across the range lower - upper . Definition at line 219 of file Analysis.cc. {
const string path = histoPath(hname);
Histo1DPtr hist = make_shared<Histo1D>(nbins, lower, upper, path, title);
addAnalysisObject(hist);
MSG_TRACE("Made histogram " << hname << " for " << name());
hist->setAnnotation("XLabel", xtitle);
hist->setAnnotation("YLabel", ytitle);
return hist;
}
Book a 1D histogram with non-uniform bins defined by the vector of bin edges binedges . Definition at line 234 of file Analysis.cc. {
const string path = histoPath(hname);
Histo1DPtr hist = make_shared<Histo1D>(binedges, path, title);
addAnalysisObject(hist);
MSG_TRACE("Made histogram " << hname << " for " << name());
hist->setAnnotation("XLabel", xtitle);
hist->setAnnotation("YLabel", ytitle);
return hist;
}
Book a 1D histogram with binning from a reference scatter. Definition at line 249 of file Analysis.cc. {
const string path = histoPath(hname);
Histo1DPtr hist = make_shared<Histo1D>(refscatter, path);
addAnalysisObject(hist);
MSG_TRACE("Made histogram " << hname << " for " << name());
hist->setTitle(title);
hist->setAnnotation("XLabel", xtitle);
hist->setAnnotation("YLabel", ytitle);
return hist;
}
Book a 1D histogram, using the binnings in the reference data histogram. Definition at line 265 of file Analysis.cc. {
const Scatter2D& refdata = refData(hname);
return bookHisto1D(hname, refdata, title, xtitle, ytitle);
}
Book a 1D histogram, using the binnings in the reference data histogram. The paper, dataset and x/y-axis IDs will be used to build the histo name in the HepData standard way. Definition at line 274 of file Analysis.cc. {
const string axisCode = makeAxisCode(datasetId, xAxisId, yAxisId);
return bookHisto1D(axisCode, title, xtitle, ytitle);
}
Book a 2D histogram with nxbins and nybins uniformly distributed across the ranges xlower - xupper and ylower - yupper respectively along the x- and y-axis.
Definition at line 289 of file Analysis.cc. {
const string path = histoPath(hname);
Histo2DPtr hist = make_shared<Histo2D>(nxbins, xlower, xupper, nybins, ylower, yupper, path, title);
addAnalysisObject(hist);
MSG_TRACE("Made 2D histogram " << hname << " for " << name());
hist->setAnnotation("XLabel", xtitle);
hist->setAnnotation("YLabel", ytitle);
hist->setAnnotation("ZLabel", ztitle);
return hist;
}
Book a 2D histogram with non-uniform bins defined by the vectorx of bin edges xbinedges and ybinedges. Definition at line 308 of file Analysis.cc. {
const string path = histoPath(hname);
Histo2DPtr hist = make_shared<Histo2D>(xbinedges, ybinedges, path, title);
addAnalysisObject(hist);
MSG_TRACE("Made 2D histogram " << hname << " for " << name());
hist->setAnnotation("XLabel", xtitle);
hist->setAnnotation("YLabel", ytitle);
hist->setAnnotation("ZLabel", ztitle);
return hist;
}
Book a 1D profile histogram with nbins uniformly distributed across the range lower - upper . Definition at line 368 of file Analysis.cc. {
const string path = histoPath(hname);
Profile1DPtr prof = make_shared<Profile1D>(nbins, lower, upper, path, title);
addAnalysisObject(prof);
MSG_TRACE("Made profile histogram " << hname << " for " << name());
prof->setAnnotation("XLabel", xtitle);
prof->setAnnotation("YLabel", ytitle);
return prof;
}
Book a 1D profile histogram with non-uniform bins defined by the vector of bin edges binedges . Definition at line 383 of file Analysis.cc. {
const string path = histoPath(hname);
Profile1DPtr prof = make_shared<Profile1D>(binedges, path, title);
addAnalysisObject(prof);
MSG_TRACE("Made profile histogram " << hname << " for " << name());
prof->setAnnotation("XLabel", xtitle);
prof->setAnnotation("YLabel", ytitle);
return prof;
}
Book a 1D profile histogram with binning from a reference scatter. Definition at line 398 of file Analysis.cc. {
const string path = histoPath(hname);
Profile1DPtr prof = make_shared<Profile1D>(refscatter, path);
addAnalysisObject(prof);
MSG_TRACE("Made profile histogram " << hname << " for " << name());
prof->setTitle(title);
prof->setAnnotation("XLabel", xtitle);
prof->setAnnotation("YLabel", ytitle);
return prof;
}
Book a 1D profile histogram, using the binnings in the reference data histogram. Definition at line 414 of file Analysis.cc. {
const Scatter2D& refdata = refData(hname);
return bookProfile1D(hname, refdata, title, xtitle, ytitle);
}
Book a 1D profile histogram, using the binnings in the reference data histogram. The paper, dataset and x/y-axis IDs will be used to build the histo name in the HepData standard way. Definition at line 423 of file Analysis.cc. {
const string axisCode = makeAxisCode(datasetId, xAxisId, yAxisId);
return bookProfile1D(axisCode, title, xtitle, ytitle);
}
Book a 2D profile histogram with nxbins and nybins uniformly distributed across the ranges xlower - xupper and ylower - yupper respectively along the x- and y-axis. Definition at line 436 of file Analysis.cc. {
const string path = histoPath(hname);
Profile2DPtr prof = make_shared<Profile2D>(nxbins, xlower, xupper, nybins, ylower, yupper, path, title);
addAnalysisObject(prof);
MSG_TRACE("Made 2D profile histogram " << hname << " for " << name());
prof->setAnnotation("XLabel", xtitle);
prof->setAnnotation("YLabel", ytitle);
prof->setAnnotation("ZLabel", ztitle);
return prof;
}
Book a 2D profile histogram with non-uniform bins defined by the vectorx of bin edges xbinedges and ybinedges. Definition at line 455 of file Analysis.cc. {
const string path = histoPath(hname);
Profile2DPtr prof = make_shared<Profile2D>(xbinedges, ybinedges, path, title);
addAnalysisObject(prof);
MSG_TRACE("Made 2D profile histogram " << hname << " for " << name());
prof->setAnnotation("XLabel", xtitle);
prof->setAnnotation("YLabel", ytitle);
prof->setAnnotation("ZLabel", ztitle);
return prof;
}
Book a 2-dimensional data point set with the given name.
Definition at line 525 of file Analysis.cc. {
Scatter2DPtr s;
const string path = histoPath(hname);
if (copy_pts) {
const Scatter2D& refdata = refData(hname);
s = make_shared<Scatter2D>(refdata, path);
foreach (Point2D& p, s->points()) p.setY(0, 0);
} else {
s = make_shared<Scatter2D>(path);
}
addAnalysisObject(s);
MSG_TRACE("Made scatter " << hname << " for " << name());
s->setTitle(title);
s->setAnnotation("XLabel", xtitle);
s->setAnnotation("YLabel", ytitle);
return s;
}
Book a 2-dimensional data point set, using the binnings in the reference data histogram. The paper, dataset and x/y-axis IDs will be used to build the histo name in the HepData standard way.
Definition at line 515 of file Analysis.cc. {
const string axisCode = makeAxisCode(datasetId, xAxisId, yAxisId);
return bookScatter2D(axisCode, copy_pts, title, xtitle, ytitle);
}
Book a 2-dimensional data point set with equally spaced x-points in a range. The y values and errors will be set to 0. Definition at line 548 of file Analysis.cc. {
const string path = histoPath(hname);
Scatter2DPtr s = make_shared<Scatter2D>(path);
const double binwidth = (upper-lower)/npts;
for (size_t pt = 0; pt < npts; ++pt) {
const double bincentre = lower + (pt + 0.5) * binwidth;
s->addPoint(bincentre, 0, binwidth/2.0, 0);
}
addAnalysisObject(s);
MSG_TRACE("Made scatter " << hname << " for " << name());
s->setTitle(title);
s->setAnnotation("XLabel", xtitle);
s->setAnnotation("YLabel", ytitle);
return s;
}
Book a 2-dimensional data point set based on provided contiguous "bin edges". The y values and errors will be set to 0. Definition at line 569 of file Analysis.cc. {
const string path = histoPath(hname);
Scatter2DPtr s = make_shared<Scatter2D>(path);
for (size_t pt = 0; pt < binedges.size()-1; ++pt) {
const double bincentre = (binedges[pt] + binedges[pt+1]) / 2.0;
const double binwidth = binedges[pt+1] - binedges[pt];
s->addPoint(bincentre, 0, binwidth/2.0, 0);
}
addAnalysisObject(s);
MSG_TRACE("Made scatter " << hname << " for " << name());
s->setTitle(title);
s->setAnnotation("XLabel", xtitle);
s->setAnnotation("YLabel", ytitle);
return s;
}
Collider on which the experiment ran. Definition at line 171 of file Analysis.hh.
Get the process cross-section in pb. Throws if this hasn't been set. Definition at line 151 of file Analysis.cc. {
if (!_gotCrossSection || std::isnan(_crossSection)) {
string errMsg = "You did not set the cross section for the analysis " + name();
throw Error(errMsg);
}
return _crossSection;
}
Get the process cross-section per generated event in pb. Throws if this hasn't been set. Definition at line 159 of file Analysis.cc. {
const double sumW = sumOfWeights();
assert(sumW != 0.0);
return _crossSection / sumW;
}
Register a contained projection (user-facing version)
Definition at line 151 of file ProjectionApplier.hh. { return declareProjection(proj, name); }
Register a contained projection. The type of the argument is used to instantiate a new projection internally: this new object is applied to events rather than the argument object. Hence you are advised to only use locally-scoped Projection objects in your Projection and Analysis constructors, and to avoid polymorphism (e.g. handling
Definition at line 142 of file ProjectionApplier.hh. {
const Projection& reg = _declareProjection(proj, name);
const PROJ& rtn = dynamic_cast<const PROJ&>(reg);
return rtn;
}
Get a full description of the analysis. Full textual description of this analysis, what it is useful for, what experimental techniques are applied, etc. Should be treated as a chunk of restructuredText (http://docutils.sourceforge.net/rst.html), with equations to be rendered as LaTeX with amsmath operators. Definition at line 152 of file Analysis.hh. {
return info().description();
}
Helper for counter division.
Definition at line 593 of file Analysis.cc.
Helper for histogram division with raw YODA objects.
Helper for histogram division.
Definition at line 606 of file Analysis.cc.
Helper for histogram division with raw YODA objects.
Helper for profile histogram division.
Definition at line 619 of file Analysis.cc.
Helper for profile histogram division with raw YODA objects.
Helper for 2D histogram division.
Definition at line 632 of file Analysis.cc.
Helper for 2D histogram division with raw YODA objects.
Helper for 2D profile histogram division.
Definition at line 645 of file Analysis.cc.
Helper for 2D profile histogram division with raw YODA objects
Helper for histogram efficiency calculation.
Definition at line 661 of file Analysis.cc. {
const string path = s->path();
*s = YODA::efficiency(*h1, *h2);
s->setPath(path);
}
Helper for histogram efficiency calculation.
Experiment which performed and published this analysis. Definition at line 166 of file Analysis.hh. {
return info().experiment();
}
function fills map EventCountsPerSR by looping over all signal regions and looking if the event falls into this signal region Definition at line 581 of file ATLAS_2012_I1204447.cc. {
// Get cut values for HTlep, loop over them and add event if cut is passed
vector<int> cut_values = getCutsPerSignalRegion("HTlep", onZ);
for (size_t i = 0; i < cut_values.size(); i++) {
if (HTlep > cut_values[i])
_eventCountsPerSR[("HTlep_" + basic_signal_region + "_cut_" + toString(cut_values[i]))] += weight;
}
// Get cut values for METStrong, loop over them and add event if cut is passed
cut_values = getCutsPerSignalRegion("METStrong", onZ);
for (size_t i = 0; i < cut_values.size(); i++) {
if (eTmiss > cut_values[i] && HTjets > 100.)
_eventCountsPerSR[("METStrong_" + basic_signal_region + "_cut_" + toString(cut_values[i]))] += weight;
}
// Get cut values for METWeak, loop over them and add event if cut is passed
cut_values = getCutsPerSignalRegion("METWeak", onZ);
for (size_t i = 0; i < cut_values.size(); i++) {
if (eTmiss > cut_values[i] && HTjets <= 100.)
_eventCountsPerSR[("METWeak_" + basic_signal_region + "_cut_" + toString(cut_values[i]))] += weight;
}
// Get cut values for Meff, loop over them and add event if cut is passed
cut_values = getCutsPerSignalRegion("Meff", onZ);
for (size_t i = 0; i < cut_values.size(); i++) {
if (meff > cut_values[i])
_eventCountsPerSR[("Meff_" + basic_signal_region + "_cut_" + toString(cut_values[i]))] += weight;
}
// Get cut values for MeffStrong, loop over them and add event if cut is passed
cut_values = getCutsPerSignalRegion("MeffStrong", onZ);
for (size_t i = 0; i < cut_values.size(); i++) {
if (meff > cut_values[i] && eTmiss > 75.)
_eventCountsPerSR[("MeffStrong_" + basic_signal_region + "_cut_" + toString(cut_values[i]))] += weight;
}
}
Normalise histograms etc., after the run. Reimplemented from Analysis. Definition at line 427 of file ATLAS_2012_I1204447.cc. {
// Normalize to an integrated luminosity of 1 fb-1
double norm = crossSection()/femtobarn/sumOfWeights();
string best_signal_region = "";
double ratio_best_SR = 0.;
// Loop over all signal regions and find signal region with best sensitivity (ratio signal events/visible cross-section)
for (size_t i = 0; i < _signal_regions.size(); i++) {
double signal_events = _eventCountsPerSR[_signal_regions[i]] * norm;
// Use expected upper limits to find best signal region
double UL95 = getUpperLimit(_signal_regions[i], false);
double ratio = signal_events / UL95;
if (ratio > ratio_best_SR) {
best_signal_region = _signal_regions[i];
ratio_best_SR = ratio;
}
}
double signal_events_best_SR = _eventCountsPerSR[best_signal_region] * norm;
double exp_UL_best_SR = getUpperLimit(best_signal_region, false);
double obs_UL_best_SR = getUpperLimit(best_signal_region, true);
// Print out result
cout << "----------------------------------------------------------------------------------------" << endl;
cout << "Best signal region: " << best_signal_region << endl;
cout << "Normalized number of signal events in this best signal region (per fb-1): " << signal_events_best_SR << endl;
cout << "Efficiency*Acceptance: " << _eventCountsPerSR[best_signal_region]/sumOfWeights() << endl;
cout << "Cross-section [fb]: " << crossSection()/femtobarn << endl;
cout << "Expected visible cross-section (per fb-1): " << exp_UL_best_SR << endl;
cout << "Ratio (signal events / expected visible cross-section): " << ratio_best_SR << endl;
cout << "Observed visible cross-section (per fb-1): " << obs_UL_best_SR << endl;
cout << "Ratio (signal events / observed visible cross-section): " << signal_events_best_SR/obs_UL_best_SR << endl;
cout << "----------------------------------------------------------------------------------------" << endl;
cout << "Using the EXPECTED limits (visible cross-section) of the analysis: " << endl;
if (signal_events_best_SR > exp_UL_best_SR) {
cout << "Since the number of signal events > the visible cross-section, this model/grid point is EXCLUDED with 95% CL." << endl;
_h_excluded->fill(1);
}
else {
cout << "Since the number of signal events < the visible cross-section, this model/grid point is NOT EXCLUDED." << endl;
_h_excluded->fill(0);
}
cout << "----------------------------------------------------------------------------------------" << endl;
cout << "Using the OBSERVED limits (visible cross-section) of the analysis: " << endl;
if (signal_events_best_SR > obs_UL_best_SR) {
cout << "Since the number of signal events > the visible cross-section, this model/grid point is EXCLUDED with 95% CL." << endl;
_h_excluded->fill(1);
}
else {
cout << "Since the number of signal events < the visible cross-section, this model/grid point is NOT EXCLUDED." << endl;
_h_excluded->fill(0);
}
cout << "----------------------------------------------------------------------------------------" << endl;
// Normalize to cross section
if (norm != 0) {
scale(_h_HTlep_all, norm);
scale(_h_HTjets_all, norm);
scale(_h_MET_all, norm);
scale(_h_Meff_all, norm);
scale(_h_pt_1_3l, norm);
scale(_h_pt_2_3l, norm);
scale(_h_pt_3_3l, norm);
scale(_h_pt_1_2ltau, norm);
scale(_h_pt_2_2ltau, norm);
scale(_h_pt_3_2ltau, norm);
scale(_h_e_n, norm);
scale(_h_mu_n, norm);
scale(_h_tau_n, norm);
scale(_h_excluded, signal_events_best_SR);
}
}
Get the named projection, specifying return type via a template argument (user-facing alias).
Definition at line 57 of file ProjectionApplier.hh. { return getProjection<PROJ>(name); }
Function calculating the prong number of taus Definition at line 638 of file ATLAS_2012_I1204447.cc. {
assert(p != NULL);
//const int tau_barcode = p->barcode();
const GenVertex* dv = p->end_vertex();
assert(dv != NULL);
for (GenVertex::particles_out_const_iterator pp = dv->particles_out_const_begin(); pp != dv->particles_out_const_end(); ++pp) {
// If they have status 1 and are charged they will produce a track and the prong number is +1
if ((*pp)->status() == 1 ) {
const int id = (*pp)->pdg_id();
if (Rivet::PID::charge(id) != 0 ) ++nprong;
// Check if tau decays leptonically
// @todo Can a tau decay include a tau in its decay daughters?!
if ((abs(id) == PID::ELECTRON || abs(id) == PID::MUON || abs(id) == PID::TAU) && abs(p->pdg_id()) == PID::TAU) lep_decaying_tau = true;
}
// If the status of the daughter particle is 2 it is unstable and the further decays are checked
else if ((*pp)->status() == 2 ) {
get_prong_number(*pp, nprong, lep_decaying_tau);
}
}
}
Function returning 4-vector of daughter-particle if it is a tau neutrino Definition at line 625 of file ATLAS_2012_I1204447.cc. {
assert(p.abspid() == PID::TAU);
const GenVertex* dv = p.genParticle()->end_vertex();
assert(dv != NULL);
for (GenVertex::particles_out_const_iterator pp = dv->particles_out_const_begin(); pp != dv->particles_out_const_end(); ++pp) {
if (abs((*pp)->pdg_id()) == PID::NU_TAU) return FourMomentum((*pp)->momentum());
}
return FourMomentum();
}
Get a data object from the histogram system
Definition at line 828 of file Analysis.hh. {
foreach (const AnalysisObjectPtr& ao, analysisObjects()) {
if (ao->path() == histoPath(name)) return dynamic_pointer_cast<AO>(ao);
}
throw Exception("Data object " + histoPath(name) + " not found");
}
Get a data object from the histogram system (non-const)
Definition at line 839 of file Analysis.hh. {
foreach (const AnalysisObjectPtr& ao, analysisObjects()) {
if (ao->path() == histoPath(name)) return dynamic_pointer_cast<AO>(ao);
}
throw Exception("Data object " + histoPath(name) + " not found");
}
Function giving all cut vales per kinematic variable (taking onZ for MET into account) Definition at line 549 of file ATLAS_2012_I1204447.cc. {
vector<int> cutValues;
// Cut values for HTlep
if (signal_region.compare("HTlep") == 0) {
cutValues.push_back(0);
cutValues.push_back(100);
cutValues.push_back(150);
cutValues.push_back(200);
cutValues.push_back(300);
}
// Cut values for METStrong (HTjets > 100 GeV) and METWeak (HTjets < 100 GeV)
else if (signal_region.compare("METStrong") == 0 || signal_region.compare("METWeak") == 0) {
if (onZ == 0) cutValues.push_back(0);
else if (onZ == 1) cutValues.push_back(20);
cutValues.push_back(50);
cutValues.push_back(75);
}
// Cut values for Meff and MeffStrong (MET > 75 GeV)
if (signal_region.compare("Meff") == 0 || signal_region.compare("MeffStrong") == 0) {
cutValues.push_back(0);
cutValues.push_back(150);
cutValues.push_back(300);
cutValues.push_back(500);
}
return cutValues;
}
Get a named Histo1D object from the histogram system. Definition at line 854 of file Analysis.hh. {
return getAnalysisObject<Histo1D>(name);
}
Get a named Histo1D object from the histogram system (non-const) Definition at line 859 of file Analysis.hh. {
return getAnalysisObject<Histo1D>(name);
}
Get a Histo1D object from the histogram system by axis ID codes (non-const) Definition at line 864 of file Analysis.hh. {
return getAnalysisObject<Histo1D>(makeAxisCode(datasetId, xAxisId, yAxisId));
}
Get a Histo1D object from the histogram system by axis ID codes (non-const) Definition at line 869 of file Analysis.hh. {
return getAnalysisObject<Histo1D>(makeAxisCode(datasetId, xAxisId, yAxisId));
}
Get a Log object based on the name() property of the calling analysis object. Reimplemented from ProjectionApplier. Definition at line 78 of file Analysis.cc. {
string logname = "Rivet.Analysis." + name();
return Log::getLog(logname);
}
Get a named Profile1D object from the histogram system. Definition at line 896 of file Analysis.hh. {
return getAnalysisObject<Profile1D>(name);
}
Get a named Profile1D object from the histogram system (non-const) Definition at line 901 of file Analysis.hh. {
return getAnalysisObject<Profile1D>(name);
}
Get a Profile1D object from the histogram system by axis ID codes (non-const) Definition at line 906 of file Analysis.hh. {
return getAnalysisObject<Profile1D>(makeAxisCode(datasetId, xAxisId, yAxisId));
}
Get a Profile1D object from the histogram system by axis ID codes (non-const) Definition at line 911 of file Analysis.hh. {
return getAnalysisObject<Profile1D>(makeAxisCode(datasetId, xAxisId, yAxisId));
}
Get the named projection, specifying return type via a template argument.
Definition at line 50 of file ProjectionApplier.hh. {
const Projection& p = getProjHandler().getProjection(*this, name);
return pcast<PROJ>(p);
}
Get the named projection (non-templated, so returns as a reference to a Projection base class). Definition at line 61 of file ProjectionApplier.hh. {
return getProjHandler().getProjection(*this, name);
}
Get the contained projections, including recursion. Definition at line 43 of file ProjectionApplier.hh. {
return getProjHandler().getChildProjections(*this, ProjectionHandler::DEEP);
}
Get a reference to the ProjectionHandler for this thread. Definition at line 122 of file ProjectionApplier.hh. {
return _projhandler;
}
Get a named Scatter2D object from the histogram system. Definition at line 938 of file Analysis.hh. {
return getAnalysisObject<Scatter2D>(name);
}
Get a named Scatter2D object from the histogram system (non-const) Definition at line 943 of file Analysis.hh. {
return getAnalysisObject<Scatter2D>(name);
}
Get a Scatter2D object from the histogram system by axis ID codes (non-const) Definition at line 948 of file Analysis.hh. {
return getAnalysisObject<Scatter2D>(makeAxisCode(datasetId, xAxisId, yAxisId));
}
Get a Scatter2D object from the histogram system by axis ID codes (non-const) Definition at line 953 of file Analysis.hh. {
return getAnalysisObject<Scatter2D>(makeAxisCode(datasetId, xAxisId, yAxisId));
}
Helper functions. Function giving a list of all signal regions Definition at line 512 of file ATLAS_2012_I1204447.cc. {
// List of basic signal regions
vector<string> basic_signal_regions;
basic_signal_regions.push_back("3l_offZ");
basic_signal_regions.push_back("3l_onZ");
basic_signal_regions.push_back("2ltau_offZ");
basic_signal_regions.push_back("2ltau_onZ");
// List of kinematic variables
vector<string> kinematic_variables;
kinematic_variables.push_back("HTlep");
kinematic_variables.push_back("METStrong");
kinematic_variables.push_back("METWeak");
kinematic_variables.push_back("Meff");
kinematic_variables.push_back("MeffStrong");
vector<string> signal_regions;
// Loop over all kinematic variables and basic signal regions
for (size_t i0 = 0; i0 < kinematic_variables.size(); i0++) {
for (size_t i1 = 0; i1 < basic_signal_regions.size(); i1++) {
// Is signal region onZ?
int onZ = (basic_signal_regions[i1].find("onZ") != string::npos) ? 1 : 0;
// Get cut values for this kinematic variable
vector<int> cut_values = getCutsPerSignalRegion(kinematic_variables[i0], onZ);
// Loop over all cut values
for (size_t i2 = 0; i2 < cut_values.size(); i2++) {
// push signal region into vector
signal_regions.push_back( (kinematic_variables[i0] + "_" + basic_signal_regions[i1] + "_cut_" + toString(i2)) );
}
}
}
return signal_regions;
}
Function giving observed upper limit (visible cross-section) Definition at line 833 of file ATLAS_2012_I1204447.cc. {
map<string,double> upperLimitsObserved;
upperLimitsObserved["HTlep_3l_offZ_cut_0"] = 11.;
upperLimitsObserved["HTlep_3l_offZ_cut_100"] = 8.7;
upperLimitsObserved["HTlep_3l_offZ_cut_150"] = 4.0;
upperLimitsObserved["HTlep_3l_offZ_cut_200"] = 4.4;
upperLimitsObserved["HTlep_3l_offZ_cut_300"] = 1.6;
upperLimitsObserved["HTlep_2ltau_offZ_cut_0"] = 25.;
upperLimitsObserved["HTlep_2ltau_offZ_cut_100"] = 14.;
upperLimitsObserved["HTlep_2ltau_offZ_cut_150"] = 6.1;
upperLimitsObserved["HTlep_2ltau_offZ_cut_200"] = 3.3;
upperLimitsObserved["HTlep_2ltau_offZ_cut_300"] = 1.2;
upperLimitsObserved["HTlep_3l_onZ_cut_0"] = 48.;
upperLimitsObserved["HTlep_3l_onZ_cut_100"] = 38.;
upperLimitsObserved["HTlep_3l_onZ_cut_150"] = 14.;
upperLimitsObserved["HTlep_3l_onZ_cut_200"] = 7.2;
upperLimitsObserved["HTlep_3l_onZ_cut_300"] = 4.5;
upperLimitsObserved["HTlep_2ltau_onZ_cut_0"] = 85.;
upperLimitsObserved["HTlep_2ltau_onZ_cut_100"] = 53.;
upperLimitsObserved["HTlep_2ltau_onZ_cut_150"] = 11.0;
upperLimitsObserved["HTlep_2ltau_onZ_cut_200"] = 5.2;
upperLimitsObserved["HTlep_2ltau_onZ_cut_300"] = 3.0;
upperLimitsObserved["METStrong_3l_offZ_cut_0"] = 2.6;
upperLimitsObserved["METStrong_3l_offZ_cut_50"] = 2.1;
upperLimitsObserved["METStrong_3l_offZ_cut_75"] = 2.1;
upperLimitsObserved["METStrong_2ltau_offZ_cut_0"] = 4.2;
upperLimitsObserved["METStrong_2ltau_offZ_cut_50"] = 3.1;
upperLimitsObserved["METStrong_2ltau_offZ_cut_75"] = 2.6;
upperLimitsObserved["METStrong_3l_onZ_cut_20"] = 11.0;
upperLimitsObserved["METStrong_3l_onZ_cut_50"] = 6.4;
upperLimitsObserved["METStrong_3l_onZ_cut_75"] = 5.1;
upperLimitsObserved["METStrong_2ltau_onZ_cut_20"] = 5.9;
upperLimitsObserved["METStrong_2ltau_onZ_cut_50"] = 3.4;
upperLimitsObserved["METStrong_2ltau_onZ_cut_75"] = 1.2;
upperLimitsObserved["METWeak_3l_offZ_cut_0"] = 11.;
upperLimitsObserved["METWeak_3l_offZ_cut_50"] = 5.3;
upperLimitsObserved["METWeak_3l_offZ_cut_75"] = 3.1;
upperLimitsObserved["METWeak_2ltau_offZ_cut_0"] = 23.;
upperLimitsObserved["METWeak_2ltau_offZ_cut_50"] = 4.3;
upperLimitsObserved["METWeak_2ltau_offZ_cut_75"] = 3.1;
upperLimitsObserved["METWeak_3l_onZ_cut_20"] = 41.;
upperLimitsObserved["METWeak_3l_onZ_cut_50"] = 16.;
upperLimitsObserved["METWeak_3l_onZ_cut_75"] = 8.0;
upperLimitsObserved["METWeak_2ltau_onZ_cut_20"] = 80.;
upperLimitsObserved["METWeak_2ltau_onZ_cut_50"] = 4.4;
upperLimitsObserved["METWeak_2ltau_onZ_cut_75"] = 1.8;
upperLimitsObserved["Meff_3l_offZ_cut_0"] = 11.;
upperLimitsObserved["Meff_3l_offZ_cut_150"] = 8.1;
upperLimitsObserved["Meff_3l_offZ_cut_300"] = 3.1;
upperLimitsObserved["Meff_3l_offZ_cut_500"] = 2.1;
upperLimitsObserved["Meff_2ltau_offZ_cut_0"] = 25.;
upperLimitsObserved["Meff_2ltau_offZ_cut_150"] = 12.;
upperLimitsObserved["Meff_2ltau_offZ_cut_300"] = 3.9;
upperLimitsObserved["Meff_2ltau_offZ_cut_500"] = 2.2;
upperLimitsObserved["Meff_3l_onZ_cut_0"] = 48.;
upperLimitsObserved["Meff_3l_onZ_cut_150"] = 37.;
upperLimitsObserved["Meff_3l_onZ_cut_300"] = 11.;
upperLimitsObserved["Meff_3l_onZ_cut_500"] = 4.8;
upperLimitsObserved["Meff_2ltau_onZ_cut_0"] = 85.;
upperLimitsObserved["Meff_2ltau_onZ_cut_150"] = 28.;
upperLimitsObserved["Meff_2ltau_onZ_cut_300"] = 5.9;
upperLimitsObserved["Meff_2ltau_onZ_cut_500"] = 1.9;
upperLimitsObserved["MeffStrong_3l_offZ_cut_0"] = 3.8;
upperLimitsObserved["MeffStrong_3l_offZ_cut_150"] = 3.8;
upperLimitsObserved["MeffStrong_3l_offZ_cut_300"] = 2.8;
upperLimitsObserved["MeffStrong_3l_offZ_cut_500"] = 2.1;
upperLimitsObserved["MeffStrong_2ltau_offZ_cut_0"] = 3.9;
upperLimitsObserved["MeffStrong_2ltau_offZ_cut_150"] = 4.0;
upperLimitsObserved["MeffStrong_2ltau_offZ_cut_300"] = 2.9;
upperLimitsObserved["MeffStrong_2ltau_offZ_cut_500"] = 1.5;
upperLimitsObserved["MeffStrong_3l_onZ_cut_0"] = 10.0;
upperLimitsObserved["MeffStrong_3l_onZ_cut_150"] = 10.0;
upperLimitsObserved["MeffStrong_3l_onZ_cut_300"] = 6.8;
upperLimitsObserved["MeffStrong_3l_onZ_cut_500"] = 3.9;
upperLimitsObserved["MeffStrong_2ltau_onZ_cut_0"] = 1.6;
upperLimitsObserved["MeffStrong_2ltau_onZ_cut_150"] = 1.4;
upperLimitsObserved["MeffStrong_2ltau_onZ_cut_300"] = 1.5;
upperLimitsObserved["MeffStrong_2ltau_onZ_cut_500"] = 0.9;
// Expected upper limits are also given but not used in this analysis
map<string,double> upperLimitsExpected;
upperLimitsExpected["HTlep_3l_offZ_cut_0"] = 11.;
upperLimitsExpected["HTlep_3l_offZ_cut_100"] = 8.5;
upperLimitsExpected["HTlep_3l_offZ_cut_150"] = 4.6;
upperLimitsExpected["HTlep_3l_offZ_cut_200"] = 3.6;
upperLimitsExpected["HTlep_3l_offZ_cut_300"] = 1.9;
upperLimitsExpected["HTlep_2ltau_offZ_cut_0"] = 23.;
upperLimitsExpected["HTlep_2ltau_offZ_cut_100"] = 14.;
upperLimitsExpected["HTlep_2ltau_offZ_cut_150"] = 6.4;
upperLimitsExpected["HTlep_2ltau_offZ_cut_200"] = 3.6;
upperLimitsExpected["HTlep_2ltau_offZ_cut_300"] = 1.5;
upperLimitsExpected["HTlep_3l_onZ_cut_0"] = 33.;
upperLimitsExpected["HTlep_3l_onZ_cut_100"] = 25.;
upperLimitsExpected["HTlep_3l_onZ_cut_150"] = 12.;
upperLimitsExpected["HTlep_3l_onZ_cut_200"] = 6.5;
upperLimitsExpected["HTlep_3l_onZ_cut_300"] = 3.1;
upperLimitsExpected["HTlep_2ltau_onZ_cut_0"] = 94.;
upperLimitsExpected["HTlep_2ltau_onZ_cut_100"] = 61.;
upperLimitsExpected["HTlep_2ltau_onZ_cut_150"] = 9.9;
upperLimitsExpected["HTlep_2ltau_onZ_cut_200"] = 4.5;
upperLimitsExpected["HTlep_2ltau_onZ_cut_300"] = 1.9;
upperLimitsExpected["METStrong_3l_offZ_cut_0"] = 3.1;
upperLimitsExpected["METStrong_3l_offZ_cut_50"] = 2.4;
upperLimitsExpected["METStrong_3l_offZ_cut_75"] = 2.3;
upperLimitsExpected["METStrong_2ltau_offZ_cut_0"] = 4.8;
upperLimitsExpected["METStrong_2ltau_offZ_cut_50"] = 3.3;
upperLimitsExpected["METStrong_2ltau_offZ_cut_75"] = 2.1;
upperLimitsExpected["METStrong_3l_onZ_cut_20"] = 8.7;
upperLimitsExpected["METStrong_3l_onZ_cut_50"] = 4.9;
upperLimitsExpected["METStrong_3l_onZ_cut_75"] = 3.8;
upperLimitsExpected["METStrong_2ltau_onZ_cut_20"] = 7.3;
upperLimitsExpected["METStrong_2ltau_onZ_cut_50"] = 2.8;
upperLimitsExpected["METStrong_2ltau_onZ_cut_75"] = 1.5;
upperLimitsExpected["METWeak_3l_offZ_cut_0"] = 10.;
upperLimitsExpected["METWeak_3l_offZ_cut_50"] = 4.7;
upperLimitsExpected["METWeak_3l_offZ_cut_75"] = 3.0;
upperLimitsExpected["METWeak_2ltau_offZ_cut_0"] = 21.;
upperLimitsExpected["METWeak_2ltau_offZ_cut_50"] = 4.0;
upperLimitsExpected["METWeak_2ltau_offZ_cut_75"] = 2.6;
upperLimitsExpected["METWeak_3l_onZ_cut_20"] = 30.;
upperLimitsExpected["METWeak_3l_onZ_cut_50"] = 10.;
upperLimitsExpected["METWeak_3l_onZ_cut_75"] = 5.4;
upperLimitsExpected["METWeak_2ltau_onZ_cut_20"] = 88.;
upperLimitsExpected["METWeak_2ltau_onZ_cut_50"] = 5.5;
upperLimitsExpected["METWeak_2ltau_onZ_cut_75"] = 2.2;
upperLimitsExpected["Meff_3l_offZ_cut_0"] = 11.;
upperLimitsExpected["Meff_3l_offZ_cut_150"] = 8.8;
upperLimitsExpected["Meff_3l_offZ_cut_300"] = 3.7;
upperLimitsExpected["Meff_3l_offZ_cut_500"] = 2.1;
upperLimitsExpected["Meff_2ltau_offZ_cut_0"] = 23.;
upperLimitsExpected["Meff_2ltau_offZ_cut_150"] = 13.;
upperLimitsExpected["Meff_2ltau_offZ_cut_300"] = 4.9;
upperLimitsExpected["Meff_2ltau_offZ_cut_500"] = 2.4;
upperLimitsExpected["Meff_3l_onZ_cut_0"] = 33.;
upperLimitsExpected["Meff_3l_onZ_cut_150"] = 25.;
upperLimitsExpected["Meff_3l_onZ_cut_300"] = 9.;
upperLimitsExpected["Meff_3l_onZ_cut_500"] = 3.9;
upperLimitsExpected["Meff_2ltau_onZ_cut_0"] = 94.;
upperLimitsExpected["Meff_2ltau_onZ_cut_150"] = 35.;
upperLimitsExpected["Meff_2ltau_onZ_cut_300"] = 6.8;
upperLimitsExpected["Meff_2ltau_onZ_cut_500"] = 2.5;
upperLimitsExpected["MeffStrong_3l_offZ_cut_0"] = 3.9;
upperLimitsExpected["MeffStrong_3l_offZ_cut_150"] = 3.9;
upperLimitsExpected["MeffStrong_3l_offZ_cut_300"] = 3.0;
upperLimitsExpected["MeffStrong_3l_offZ_cut_500"] = 2.0;
upperLimitsExpected["MeffStrong_2ltau_offZ_cut_0"] = 3.8;
upperLimitsExpected["MeffStrong_2ltau_offZ_cut_150"] = 3.9;
upperLimitsExpected["MeffStrong_2ltau_offZ_cut_300"] = 3.1;
upperLimitsExpected["MeffStrong_2ltau_offZ_cut_500"] = 1.6;
upperLimitsExpected["MeffStrong_3l_onZ_cut_0"] = 6.9;
upperLimitsExpected["MeffStrong_3l_onZ_cut_150"] = 7.1;
upperLimitsExpected["MeffStrong_3l_onZ_cut_300"] = 4.9;
upperLimitsExpected["MeffStrong_3l_onZ_cut_500"] = 3.0;
upperLimitsExpected["MeffStrong_2ltau_onZ_cut_0"] = 2.4;
upperLimitsExpected["MeffStrong_2ltau_onZ_cut_150"] = 2.5;
upperLimitsExpected["MeffStrong_2ltau_onZ_cut_300"] = 2.0;
upperLimitsExpected["MeffStrong_2ltau_onZ_cut_500"] = 1.1;
if (observed) return upperLimitsObserved[signal_region];
else return upperLimitsExpected[signal_region];
}
Access the controlling AnalysisHandler object. Definition at line 289 of file Analysis.hh. { return *_analysishandler; }
Get the canonical histogram "directory" path for this analysis.
Definition at line 38 of file Analysis.cc. {
/// @todo Cache in a member variable
string _histoDir;
if (_histoDir.empty()) {
_histoDir = "/" + name();
if (handler().runName().length() > 0) {
_histoDir = "/" + handler().runName() + _histoDir;
}
replace_all(_histoDir, "//", "/"); //< iterates until none
}
return _histoDir;
}
Get the canonical histogram path for the named histogram in this analysis. Definition at line 52 of file Analysis.cc. {
const string path = histoDir() + "/" + hname;
return path;
}
Get the canonical histogram path for the numbered histogram in this analysis. Definition at line 58 of file Analysis.cc. {
return histoDir() + "/" + makeAxisCode(datasetId, xAxisId, yAxisId);
}
Get the actual AnalysisInfo object in which all this metadata is stored. Definition at line 105 of file Analysis.hh.
Get the actual AnalysisInfo object in which all this metadata is stored (non-const). Definition at line 247 of file Analysis.hh.
Book histograms and initialise projections before the run. Reimplemented from Analysis. Definition at line 23 of file ATLAS_2012_I1204447.cc. {
// To calculate the acceptance without having the fiducial lepton efficiencies included, this part can be turned off
_use_fiducial_lepton_efficiency = true;
// Random numbers for simulation of ATLAS detector reconstruction efficiency
srand(160385);
// Read in all signal regions
_signal_regions = getSignalRegions();
// Set number of events per signal region to 0
for (size_t i = 0; i < _signal_regions.size(); i++)
_eventCountsPerSR[_signal_regions[i]] = 0.0;
// Final state including all charged and neutral particles
const FinalState fs(-5.0, 5.0, 1*GeV);
declare(fs, "FS");
// Final state including all charged particles
declare(ChargedFinalState(Cuts::abseta < 2.5 && Cuts::pT > 1*GeV), "CFS");
// Final state including all visible particles (to calculate MET, Jets etc.)
declare(VisibleFinalState(Cuts::abseta < 5.0), "VFS");
// Final state including all AntiKt 04 Jets
VetoedFinalState vfs;
vfs.addVetoPairId(PID::MUON);
declare(FastJets(vfs, FastJets::ANTIKT, 0.4), "AntiKtJets04");
// Final state including all unstable particles (including taus)
declare(UnstableFinalState(Cuts::abseta < 5.0 && Cuts::pT > 5*GeV), "UFS");
// Final state including all electrons
IdentifiedFinalState elecs(Cuts::abseta < 2.47 && Cuts::pT > 10*GeV);
elecs.acceptIdPair(PID::ELECTRON);
declare(elecs, "elecs");
// Final state including all muons
IdentifiedFinalState muons(Cuts::abseta < 2.5 && Cuts::pT > 10*GeV);
muons.acceptIdPair(PID::MUON);
declare(muons, "muons");
// Book histograms
_h_HTlep_all = bookHisto1D("HTlep_all" , 30, 0, 1500);
_h_HTjets_all = bookHisto1D("HTjets_all", 30, 0, 1500);
_h_MET_all = bookHisto1D("MET_all" , 20, 0, 1000);
_h_Meff_all = bookHisto1D("Meff_all" , 30, 0, 3000);
_h_e_n = bookHisto1D("e_n" , 10, -0.5, 9.5);
_h_mu_n = bookHisto1D("mu_n" , 10, -0.5, 9.5);
_h_tau_n = bookHisto1D("tau_n", 10, -0.5, 9.5);
_h_pt_1_3l = bookHisto1D("pt_1_3l", 100, 0, 2000);
_h_pt_2_3l = bookHisto1D("pt_2_3l", 100, 0, 2000);
_h_pt_3_3l = bookHisto1D("pt_3_3l", 100, 0, 2000);
_h_pt_1_2ltau = bookHisto1D("pt_1_2ltau", 100, 0, 2000);
_h_pt_2_2ltau = bookHisto1D("pt_2_2ltau", 100, 0, 2000);
_h_pt_3_2ltau = bookHisto1D("pt_3_2ltau", 100, 0, 2000);
_h_excluded = bookHisto1D("excluded", 2, -0.5, 1.5);
}
Get the Inspire ID code for this analysis. Definition at line 120 of file Analysis.hh.
Helper for converting a differential histo to an integral one.
Definition at line 774 of file Analysis.cc.
Helper for converting a differential histo to an integral one.
Definition at line 781 of file Analysis.cc.
Check if analysis is compatible with the provided beam particle IDs and energies. Definition at line 97 of file Analysis.cc. {
return isCompatible(beams.first.pid(), beams.second.pid(),
beams.first.energy(), beams.second.energy());
}
Check if analysis is compatible with the provided beam particle IDs and energies. Definition at line 103 of file Analysis.cc. {
PdgIdPair beams(beam1, beam2);
pair<double,double> energies(e1, e2);
return isCompatible(beams, energies);
}
Check if analysis is compatible with the provided beam particle IDs and energies. Function checking if there is an OSSF lepton pair or a combination of 3 leptons with an invariant mass close to the Z mass
Definition at line 998 of file ATLAS_2012_I1204447.cc. {
int onZ = 0;
double best_mass_2 = 999.;
double best_mass_3 = 999.;
// Loop over all 2 particle combinations to find invariant mass of OSSF pair closest to Z mass
foreach ( const Particle& p1, particles ) {
foreach ( const Particle& p2, particles ) {
double mass_difference_2_old = fabs(91.0 - best_mass_2);
double mass_difference_2_new = fabs(91.0 - (p1.momentum() + p2.momentum()).mass()/GeV);
// If particle combination is OSSF pair calculate mass difference to Z mass
if ( (p1.pid()*p2.pid() == -121 || p1.pid()*p2.pid() == -169) ) {
// Get invariant mass closest to Z mass
if (mass_difference_2_new < mass_difference_2_old)
best_mass_2 = (p1.momentum() + p2.momentum()).mass()/GeV;
// In case there is an OSSF pair take also 3rd lepton into account (e.g. from FSR and photon to electron conversion)
foreach ( const Particle & p3 , particles ) {
double mass_difference_3_old = fabs(91.0 - best_mass_3);
double mass_difference_3_new = fabs(91.0 - (p1.momentum() + p2.momentum() + p3.momentum()).mass()/GeV);
if (mass_difference_3_new < mass_difference_3_old)
best_mass_3 = (p1.momentum() + p2.momentum() + p3.momentum()).mass()/GeV;
}
}
}
}
// Pick the minimum invariant mass of the best OSSF pair combination and the best 3 lepton combination
// If this mass is in a 20 GeV window around the Z mass, the event is classified as onZ
double best_mass = min(best_mass_2, best_mass_3);
if (fabs(91.0 - best_mass) < 20) onZ = 1;
return onZ;
}
Get the internal histogram name for given d, x and y (cf. HepData) Definition at line 63 of file Analysis.cc. {
stringstream axisCode;
axisCode << "d";
if (datasetId < 10) axisCode << 0;
axisCode << datasetId;
axisCode << "-x";
if (xAxisId < 10) axisCode << 0;
axisCode << xAxisId;
axisCode << "-y";
if (yAxisId < 10) axisCode << 0;
axisCode << yAxisId;
return axisCode.str();
}
Mark object as owned by the _projhandler
Definition at line 111 of file ProjectionApplier.hh. { _owned = true; }
Get the name of the analysis. By default this is computed by combining the results of the experiment, year and Spires ID metadata methods and you should only override it if there's a good reason why those won't work. Implements ProjectionApplier. Definition at line 115 of file Analysis.hh. {
return (info().name().empty()) ? _defaultname : info().name();
}
Return true if this analysis needs to know the process cross-section.
Definition at line 230 of file Analysis.hh. {
return info().needsCrossSection();
}
Normalize the given histogram, histo, to area = norm. Definition at line 706 of file Analysis.cc. {
if (!histo) {
MSG_WARNING("Failed to normalize histo=NULL in analysis " << name() << " (norm=" << norm << ")");
return;
}
MSG_TRACE("Normalizing histo " << histo->path() << " to " << norm);
try {
histo->normalize(norm, includeoverflows);
} catch (YODA::Exception& we) {
MSG_WARNING("Could not normalize histo " << histo->path());
return;
}
}
Normalize the given histograms, histos, to area = norm.
Definition at line 662 of file Analysis.hh. {
for (auto& h : histos) normalize(h, norm, includeoverflows);
}
Definition at line 667 of file Analysis.hh. {
for (auto& h : histos) normalize(h, norm, includeoverflows);
}
Normalize the given histogram, histo, to area = norm. Definition at line 740 of file Analysis.cc. {
if (!histo) {
MSG_ERROR("Failed to normalize histo=NULL in analysis " << name() << " (norm=" << norm << ")");
return;
}
MSG_TRACE("Normalizing histo " << histo->path() << " to " << norm);
try {
histo->normalize(norm, includeoverflows);
} catch (YODA::Exception& we) {
MSG_WARNING("Could not normalize histo " << histo->path());
return;
}
}
Normalize the given histograms, histos, to area = norm.
Definition at line 693 of file Analysis.hh. {
for (auto& h : histos) normalize(h, norm, includeoverflows);
}
Definition at line 698 of file Analysis.hh. {
for (auto& h : histos) normalize(h, norm, includeoverflows);
}
Get the number of events seen (via the analysis handler). Use in the finalize phase only. Definition at line 84 of file Analysis.cc.
Get reference data for a named histo
Definition at line 179 of file Analysis.cc.
Get reference data for a numbered histo
Definition at line 190 of file Analysis.cc. {
const string hname = makeAxisCode(datasetId, xAxisId, yAxisId);
return refData(hname);
}
Get reference data for a named histo
Definition at line 350 of file Analysis.hh.
Get reference data for a numbered histo
Definition at line 366 of file Analysis.hh. {
const string hname = makeAxisCode(datasetId, xAxisId, yAxisId);
return refData(hname);
}
Journal, and preprint references. Definition at line 181 of file Analysis.hh. {
return info().references();
}
Unregister a data object from the histogram system (by name) Definition at line 799 of file Analysis.cc. {
for (vector<AnalysisObjectPtr>::iterator it = _analysisobjects.begin(); it != _analysisobjects.end(); ++it) {
if ((*it)->path() == path) {
_analysisobjects.erase(it);
break;
}
}
}
Unregister a data object from the histogram system (by pointer) Definition at line 808 of file Analysis.cc. {
for (vector<AnalysisObjectPtr>::iterator it = _analysisobjects.begin(); it != _analysisobjects.end(); ++it) {
if (*it == ao) {
_analysisobjects.erase(it);
break;
}
}
}
Return the allowed pairs of incoming beams required by this analysis. Definition at line 207 of file Analysis.hh.
Sets of valid beam energy pairs, in GeV. Definition at line 218 of file Analysis.hh.
Information about the events needed as input for this analysis. Event types, energies, kinematic cuts, particles to be considered stable, etc. etc. Should be treated as a restructuredText bullet list (http://docutils.sourceforge.net/rst.html) Definition at line 161 of file Analysis.hh.
Multiplicatively scale the given counter, cnt, by factor factor. Definition at line 687 of file Analysis.cc. {
if (!cnt) {
MSG_WARNING("Failed to scale counter=NULL in analysis " << name() << " (scale=" << factor << ")");
return;
}
if (std::isnan(factor) || std::isinf(factor)) {
MSG_WARNING("Failed to scale counter=" << cnt->path() << " in analysis: " << name() << " (invalid scale factor = " << factor << ")");
factor = 0;
}
MSG_TRACE("Scaling counter " << cnt->path() << " by factor " << factor);
try {
cnt->scaleW(factor);
} catch (YODA::Exception& we) {
MSG_WARNING("Could not scale counter " << cnt->path());
return;
}
}
Multiplicatively scale the given counters, cnts, by factor factor.
Definition at line 645 of file Analysis.hh. {
for (auto& c : cnts) scale(c, factor);
}
Definition at line 650 of file Analysis.hh. {
// for (size_t i = 0; i < std::extent<decltype(cnts)>::value; ++i) scale(cnts[i], factor);
for (auto& c : cnts) scale(c, factor);
}
Multiplicatively scale the given histogram, histo, by factor factor. Definition at line 721 of file Analysis.cc. {
if (!histo) {
MSG_WARNING("Failed to scale histo=NULL in analysis " << name() << " (scale=" << factor << ")");
return;
}
if (std::isnan(factor) || std::isinf(factor)) {
MSG_WARNING("Failed to scale histo=" << histo->path() << " in analysis: " << name() << " (invalid scale factor = " << factor << ")");
factor = 0;
}
MSG_TRACE("Scaling histo " << histo->path() << " by factor " << factor);
try {
histo->scaleW(factor);
} catch (YODA::Exception& we) {
MSG_WARNING("Could not scale histo " << histo->path());
return;
}
}
Multiplicatively scale the given histograms, histos, by factor factor.
Definition at line 677 of file Analysis.hh. {
for (auto& h : histos) scale(h, factor);
}
Multiplicatively scale the given histogram, histo, by factor factor. Definition at line 755 of file Analysis.cc. {
if (!histo) {
MSG_ERROR("Failed to scale histo=NULL in analysis " << name() << " (scale=" << factor << ")");
return;
}
if (std::isnan(factor) || std::isinf(factor)) {
MSG_ERROR("Failed to scale histo=" << histo->path() << " in analysis: " << name() << " (invalid scale factor = " << factor << ")");
factor = 0;
}
MSG_TRACE("Scaling histo " << histo->path() << " by factor " << factor);
try {
histo->scaleW(factor);
} catch (YODA::Exception& we) {
MSG_WARNING("Could not scale histo " << histo->path());
return;
}
}
Multiplicatively scale the given histograms, histos, by factor factor.
Definition at line 708 of file Analysis.hh. {
for (auto& h : histos) scale(h, factor);
}
Set the cross section from the generator. Definition at line 145 of file Analysis.cc. {
_crossSection = xs;
_gotCrossSection = true;
return *this;
}
Declare whether this analysis needs to know the process cross-section from the generator.
Definition at line 235 of file Analysis.hh. {
info().setNeedsCrossSection(needed);
return *this;
}
Declare the allowed pairs of incoming beams required by this analysis. Definition at line 211 of file Analysis.hh.
Declare the list of valid beam energy pairs, in GeV. Definition at line 222 of file Analysis.hh. {
info().setEnergies(requiredEnergies);
return *this;
}
Get the SPIRES ID code for this analysis (~deprecated). Definition at line 125 of file Analysis.hh.
Centre of mass energy for this run. Definition at line 25 of file Analysis.cc.
Get a short description of the analysis. Short (one sentence) description used as an index entry. Use description() to provide full descriptive paragraphs of analysis details. Definition at line 142 of file Analysis.hh.
Get the sum of event weights seen (via the analysis handler). Use in the finalize phase only. Definition at line 89 of file Analysis.cc. {
return handler().sumOfWeights();
}
Any work to be done on this analysis. Definition at line 201 of file Analysis.hh.
When the original experimental analysis was published. Definition at line 176 of file Analysis.hh. Member Data Documentation
Flag to forbid projection registration in analyses until the init phase. Definition at line 176 of file ProjectionApplier.hh.
Definition at line 1052 of file ATLAS_2012_I1204447.cc.
Definition at line 1043 of file ATLAS_2012_I1204447.cc.
Definition at line 1044 of file ATLAS_2012_I1204447.cc.
Definition at line 1041 of file ATLAS_2012_I1204447.cc.
Histograms. Definition at line 1041 of file ATLAS_2012_I1204447.cc.
Definition at line 1041 of file ATLAS_2012_I1204447.cc.
Definition at line 1041 of file ATLAS_2012_I1204447.cc.
Definition at line 1043 of file ATLAS_2012_I1204447.cc.
Definition at line 1042 of file ATLAS_2012_I1204447.cc.
Definition at line 1042 of file ATLAS_2012_I1204447.cc.
Definition at line 1042 of file ATLAS_2012_I1204447.cc.
Definition at line 1042 of file ATLAS_2012_I1204447.cc.
Definition at line 1042 of file ATLAS_2012_I1204447.cc.
Definition at line 1042 of file ATLAS_2012_I1204447.cc.
Definition at line 1043 of file ATLAS_2012_I1204447.cc.
List of signal regions and event counts per signal region. Definition at line 1051 of file ATLAS_2012_I1204447.cc.
Fiducial efficiencies to model the effects of the ATLAS detector. Definition at line 1048 of file ATLAS_2012_I1204447.cc. The documentation for this class was generated from the following file: Generated on Tue Dec 13 2016 16:33:11 for The Rivet MC analysis system by 
1.7.6.1
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