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#include <SETPatternRecognition.h>
Public Member Functions | |
| virtual void | produce (const edm::Event &event, const edm::EventSetup &eSetup, std::vector< MuonRecHitContainer > &result) |
| bool | segmentCleaning (const DetId &detId, const LocalPoint &localPosition, const LocalError &localError, const LocalVector &localDirection, const LocalError &localDirectionError, const double &chi2, const int &ndf) |
| SETPatternRecognition (const edm::ParameterSet &pset) | |
| void | setServiceProxy (MuonServiceProxy *service) |
| virtual | ~SETPatternRecognition () |
Private Attributes | |
| edm::InputTag | CSCRecSegmentLabel |
| edm::InputTag | DTRecSegmentLabel |
| int | maxActiveChambers |
| double | minLocalSegmentAngle |
| double | outsideChamberErrorScale |
| edm::InputTag | RPCRecSegmentLabel |
| MuonServiceProxy * | theService |
| bool | useRPCs |
I. Bloch, E. James, S. Stoynev
Definition at line 8 of file SETPatternRecognition.h.
| SETPatternRecognition::SETPatternRecognition | ( | const edm::ParameterSet & | pset | ) | [explicit] |
Definition at line 22 of file SETPatternRecognition.cc.
References CSCRecSegmentLabel, DTRecSegmentLabel, edm::ParameterSet::getParameter(), maxActiveChambers, metname, minLocalSegmentAngle, outsideChamberErrorScale, RPCRecSegmentLabel, and useRPCs.
: MuonSeedVPatternRecognition(parameterSet.getParameter<ParameterSet>("SETTrajBuilderParameters").getParameter<ParameterSet>("FilterParameters")) { const string metname = "Muon|RecoMuon|SETPatternRecognition"; // Parameter set for the Builder ParameterSet trajectoryBuilderParameters = parameterSet.getParameter<ParameterSet>("SETTrajBuilderParameters"); // The inward-outward fitter (starts from seed state) ParameterSet filterPSet = trajectoryBuilderParameters.getParameter<ParameterSet>("FilterParameters"); maxActiveChambers = filterPSet.getParameter<int>("maxActiveChambers"); useRPCs = filterPSet.getParameter<bool>("EnableRPCMeasurement"); DTRecSegmentLabel = filterPSet.getParameter<edm::InputTag>("DTRecSegmentLabel"); CSCRecSegmentLabel = filterPSet.getParameter<edm::InputTag>("CSCRecSegmentLabel"); RPCRecSegmentLabel = filterPSet.getParameter<edm::InputTag>("RPCRecSegmentLabel"); outsideChamberErrorScale = filterPSet.getParameter<double>("OutsideChamberErrorScale"); minLocalSegmentAngle = filterPSet.getParameter<double>("MinLocalSegmentAngle"); //---- }
| virtual SETPatternRecognition::~SETPatternRecognition | ( | ) | [inline, virtual] |
Definition at line 12 of file SETPatternRecognition.h.
{}
| void SETPatternRecognition::produce | ( | const edm::Event & | event, |
| const edm::EventSetup & | eSetup, | ||
| std::vector< MuonRecHitContainer > & | result | ||
| ) | [virtual] |
Output is a cluster, with possibly more than one hit per layer
Implements MuonSeedVPatternRecognition.
Definition at line 43 of file SETPatternRecognition.cc.
References begin, CSCRecSegmentLabel, cscSegments_cfi::cscSegments, DTRecSegmentLabel, end, edm::eventsetup::heterocontainer::insert(), maxActiveChambers, metname, Pi, python::rpcRecHits_cfi::rpcRecHits, RPCRecSegmentLabel, segmentCleaning(), findQualityFiles::size, MuonTransientTrackingRecHit::specificBuild(), groupFilesInBlocks::temp, theService, and useRPCs.
Referenced by SETMuonSeedProducer::produce().
{
const string metname = "Muon|RecoMuon|SETMuonSeedSeed";
//---- Build collection of all segments
MuonRecHitContainer muonRecHits;
MuonRecHitContainer muonRecHits_DT2D_hasPhi;
MuonRecHitContainer muonRecHits_DT2D_hasZed;
MuonRecHitContainer muonRecHits_RPC;
// ********************************************;
// Get the DT-Segment collection from the Event
// ********************************************;
edm::Handle<DTRecSegment4DCollection> dtRecHits;
event.getByLabel(DTRecSegmentLabel, dtRecHits);
std::vector<DTChamberId> chambers_DT;
std::vector<DTChamberId>::const_iterator chIt_DT;
for (DTRecSegment4DCollection::const_iterator rechit = dtRecHits->begin(); rechit!=dtRecHits->end();++rechit) {
bool insert = true;
for(chIt_DT=chambers_DT.begin(); chIt_DT != chambers_DT.end(); ++chIt_DT){
if (
((*rechit).chamberId().wheel()) == ((*chIt_DT).wheel()) &&
((*rechit).chamberId().station() == (*chIt_DT).station()) &&
((*rechit).chamberId().sector() == (*chIt_DT).sector())){
insert = false;
}
}
if (insert){
chambers_DT.push_back((*rechit).chamberId());
}
if(segmentCleaning((*rechit).geographicalId(),
rechit->localPosition(), rechit->localPositionError(),
rechit->localDirection(), rechit->localDirectionError(),
rechit->chi2(), rechit->degreesOfFreedom())){
continue;
}
if( (rechit->hasZed() && rechit->hasPhi()) ) {
muonRecHits.push_back(MuonTransientTrackingRecHit::specificBuild(theService->trackingGeometry()->idToDet((*rechit).geographicalId()),&*rechit));
}
else if(rechit->hasZed()) {
muonRecHits_DT2D_hasZed.push_back(MuonTransientTrackingRecHit::specificBuild(theService->trackingGeometry()->idToDet((*rechit).geographicalId()),&*rechit));
}
else if(rechit->hasPhi()) { // safeguard
muonRecHits_DT2D_hasPhi.push_back(MuonTransientTrackingRecHit::specificBuild(theService->trackingGeometry()->idToDet((*rechit).geographicalId()),&*rechit));
}
else {
//std::cout<<"Warning in "<<metname<<": DT segment which claims to have neither phi nor Z."<<std::endl;
}
}
//std::cout<<"DT done"<<std::endl;
// ********************************************;
// Get the CSC-Segment collection from the event
// ********************************************;
edm::Handle<CSCSegmentCollection> cscSegments;
event.getByLabel(CSCRecSegmentLabel, cscSegments);
std::vector<CSCDetId> chambers_CSC;
std::vector<CSCDetId>::const_iterator chIt_CSC;
for(CSCSegmentCollection::const_iterator rechit=cscSegments->begin(); rechit != cscSegments->end(); ++rechit) {
bool insert = true;
for(chIt_CSC=chambers_CSC.begin(); chIt_CSC != chambers_CSC.end(); ++chIt_CSC){
if (((*rechit).cscDetId().chamber() == (*chIt_CSC).chamber()) &&
((*rechit).cscDetId().station() == (*chIt_CSC).station()) &&
((*rechit).cscDetId().ring() == (*chIt_CSC).ring()) &&
((*rechit).cscDetId().endcap() == (*chIt_CSC).endcap())){
insert = false;
}
}
if (insert){
chambers_CSC.push_back((*rechit).cscDetId().chamberId());
}
if(segmentCleaning((*rechit).geographicalId(),
rechit->localPosition(), rechit->localPositionError(),
rechit->localDirection(), rechit->localDirectionError(),
rechit->chi2(), rechit->degreesOfFreedom())){
continue;
}
muonRecHits.push_back(MuonTransientTrackingRecHit::specificBuild(theService->trackingGeometry()->idToDet((*rechit).geographicalId()),&*rechit));
}
//std::cout<<"CSC done"<<std::endl;
// ********************************************;
// Get the RPC-Hit collection from the event
// ********************************************;
edm::Handle<RPCRecHitCollection> rpcRecHits;
event.getByLabel(RPCRecSegmentLabel, rpcRecHits);
if(useRPCs){
for(RPCRecHitCollection::const_iterator rechit=rpcRecHits->begin(); rechit != rpcRecHits->end(); ++rechit) {
// RPCs are special
const LocalVector localDirection(0.,0.,1.);
const LocalError localDirectionError (0.,0.,0.);
const double chi2 = 1.;
const int ndf = 1;
if(segmentCleaning((*rechit).geographicalId(),
rechit->localPosition(), rechit->localPositionError(),
localDirection, localDirectionError,
chi2, ndf)){
continue;
}
muonRecHits_RPC.push_back(MuonTransientTrackingRecHit::specificBuild(theService->trackingGeometry()->idToDet((*rechit).geographicalId()),&*rechit));
}
}
//std::cout<<"RPC done"<<std::endl;
//
if(int(chambers_DT.size() + chambers_CSC.size()) > maxActiveChambers){
// std::cout <<" Too many active chambers : nDT = "<<chambers_DT.size()<<
// " nCSC = "<<chambers_CSC.size()<<" Skip them all."<<std::endl;
edm::LogWarning("tooManyActiveChambers")<<" Too many active chambers : nDT = "<<chambers_DT.size()
<<" nCSC = "<<chambers_CSC.size()<<" Skip them all.";
muonRecHits.clear();
muonRecHits_DT2D_hasPhi.clear();
muonRecHits_DT2D_hasZed.clear();
muonRecHits_RPC.clear();
}
//---- Find "pre-clusters" from all segments; these contain potential muon candidates
//---- From all the hits (i.e. segments; sometimes "rechits" is also used with the same meaning;
//---- this convention has meaning in the global reconstruction though could be misleading
//---- from a local reconstruction point of view; "local rechits" are used in the backward fit only)
//---- make clusters of hits; a trajectory could contain hits from one cluster only
// the clustering procedure is very similar to the one used in the segment reconstruction
bool useDT2D_hasPhi = true;
bool useDT2D_hasZed = true;
double dXclusBoxMax = 0.60; // phi - can be as large as 15 - 20 degrees for 6 GeV muons
double dYclusBoxMax = 0.;
// this is the main selection criteria; the value of 0.02 rad seems wide enough to
// contain any hit from a passing muon and still narrow enough to remove good part of
// possible "junk" hits
// (Comment: it may be better to allow maximum difference between any two hits in a trajectory
// to be 0.02 or 0.04 or ...; currently the requirement below is imposed on two consecutive hits)
dYclusBoxMax = 0.02; // theta // hardoded - remove it!
// X and Y are distance variables - we use eta and phi here
float dXclus = 0.0;
float dXclus_box = 0.0;
float dYclus_box = 0.0;
MuonRecHitContainer temp;
std::vector< MuonRecHitContainer > seeds;
std::vector<float> running_meanX;
std::vector<float> running_meanY;
std::vector<float> seed_minX;
std::vector<float> seed_maxX;
std::vector<float> seed_minY;
std::vector<float> seed_maxY;
// split rechits into subvectors and return vector of vectors:
// Loop over rechits
// Create one seed per hit
for (MuonRecHitContainer::const_iterator it = muonRecHits.begin(); it != muonRecHits.end(); ++it ) {
// try to avoid using 2D DT segments. We will add them later to the
// clusters they are most likely to belong to. Might need to add them
// to more than just one cluster, if we find them to be consistent with
// more than one. This would lead to an implicit sharing of hits between
// SA muon candidates.
temp.clear();
temp.push_back((*it));
seeds.push_back(temp);
// First added hit in seed defines the mean to which the next hit is compared
// for this seed.
running_meanX.push_back( (*it)->globalPosition().phi() );
running_meanY.push_back( (*it)->globalPosition().theta() );
// set min/max X and Y for box containing the hits in the precluster:
seed_minX.push_back( (*it)->globalPosition().phi() );
seed_maxX.push_back( (*it)->globalPosition().phi() );
seed_minY.push_back( (*it)->globalPosition().theta() );
seed_maxY.push_back( (*it)->globalPosition().theta() );
}
// merge clusters that are too close
// measure distance between final "running mean"
for(unsigned int NNN = 0; NNN < seeds.size(); ++NNN) {
for(unsigned int MMM = NNN+1; MMM < seeds.size(); ++MMM) {
if(running_meanX[MMM] == 999999. || running_meanX[NNN] == 999999. ) {
// LogDebug("CSC") << "CSCSegmentST::clusterHits: Warning: Skipping used seeds, this should happen - inform developers!\n";
//std::cout<<"We should never see this line now!!!"<<std::endl;
continue; //skip seeds that have been used
}
// Some complications for using phi as a clustering variable due to wrap-around (-pi = pi)
// Define temporary mean, min, and max variables for the cluster which could be merged (NNN)
double temp_meanX = running_meanX[NNN];
double temp_minX = seed_minX[NNN];
double temp_maxX = seed_maxX[NNN];
// check if the difference between the two phi values is greater than pi
// if so, need to shift temporary values by 2*pi to make a valid comparison
dXclus = running_meanX[NNN] - running_meanX[MMM];
if (dXclus > TMath::Pi()) {
temp_meanX = temp_meanX - 2.*TMath::Pi();
temp_minX = temp_minX - 2.*TMath::Pi();
temp_maxX = temp_maxX - 2.*TMath::Pi();
}
if (dXclus < -TMath::Pi()) {
temp_meanX = temp_meanX + 2.*TMath::Pi();
temp_minX = temp_minX + 2.*TMath::Pi();
temp_maxX = temp_maxX + 2.*TMath::Pi();
}
// // calculate cut criteria for simple running mean distance cut:
// // not sure that these values are really used anywhere
// calculate minmal distance between precluster boxes containing the hits:
// use the temp variables from above for phi of the NNN cluster
if ( temp_meanX > running_meanX[MMM] ) dXclus_box = temp_minX - seed_maxX[MMM];
else dXclus_box = seed_minX[MMM] - temp_maxX;
if ( running_meanY[NNN] > running_meanY[MMM] ) dYclus_box = seed_minY[NNN] - seed_maxY[MMM];
else dYclus_box = seed_minY[MMM] - seed_maxY[NNN];
if( dXclus_box < dXclusBoxMax && dYclus_box < dYclusBoxMax ) {
// merge clusters!
// merge by adding seed NNN to seed MMM and erasing seed NNN
// calculate running mean for the merged seed:
// use the temp variables from above for phi of the NNN cluster
running_meanX[MMM] = (temp_meanX*seeds[NNN].size() + running_meanX[MMM]*seeds[MMM].size()) / (seeds[NNN].size()+seeds[MMM].size());
running_meanY[MMM] = (running_meanY[NNN]*seeds[NNN].size() + running_meanY[MMM]*seeds[MMM].size()) / (seeds[NNN].size()+seeds[MMM].size());
// update min/max X and Y for box containing the hits in the merged cluster:
// use the temp variables from above for phi of the NNN cluster
if ( temp_minX <= seed_minX[MMM] ) seed_minX[MMM] = temp_minX;
if ( temp_maxX > seed_maxX[MMM] ) seed_maxX[MMM] = temp_maxX;
if ( seed_minY[NNN] <= seed_minY[MMM] ) seed_minY[MMM] = seed_minY[NNN];
if ( seed_maxY[NNN] > seed_maxY[MMM] ) seed_maxY[MMM] = seed_maxY[NNN];
// now check to see if the running mean has moved outside of the allowed -pi to pi region
// if so, then adjust shift all values up or down by 2 * pi
if (running_meanX[MMM] > TMath::Pi()) {
running_meanX[MMM] = running_meanX[MMM] - 2.*TMath::Pi();
seed_minX[MMM] = seed_minX[MMM] - 2.*TMath::Pi();
seed_maxX[MMM] = seed_maxX[MMM] - 2.*TMath::Pi();
}
if (running_meanX[MMM] < -TMath::Pi()) {
running_meanX[MMM] = running_meanX[MMM] + 2.*TMath::Pi();
seed_minX[MMM] = seed_minX[MMM] + 2.*TMath::Pi();
seed_maxX[MMM] = seed_maxX[MMM] + 2.*TMath::Pi();
}
// add seed NNN to MMM (lower to larger number)
seeds[MMM].insert(seeds[MMM].end(),seeds[NNN].begin(),seeds[NNN].end());
// mark seed NNN as used (at the moment just set running mean to 999999.)
running_meanX[NNN] = 999999.;
running_meanY[NNN] = 999999.;
// we have merged a seed (NNN) to the highter seed (MMM) - need to contimue to
// next seed (NNN+1)
break;
}
}
}
bool tooCloseClusters = false;
if(seeds.size()>1){
std::vector <double> seedTheta(seeds.size());
for(unsigned int iSeed = 0;iSeed<seeds.size();++iSeed){
seedTheta[iSeed] = seeds[iSeed][0]->globalPosition().theta();
if(iSeed){
double dTheta = fabs(seedTheta[iSeed] - seedTheta[iSeed-1]);
if (dTheta < 0.5){ //? should be something more clever
tooCloseClusters = true;
break;
}
}
}
}
// have formed clusters from all hits except for 2D DT segments. Now add the 2D segments to the
// compatible clusters. For this we compare the mean cluster postition with the
// 2D segment position. We should use the valid coordinate only and use the bad coordinate
// as a cross check.
for(unsigned int NNN = 0; NNN < seeds.size(); ++NNN) {
if(running_meanX[NNN] == 999999.) continue; //skip seeds that have been marked as used up in merging
// We have a valid cluster - loop over all 2D segments.
if(useDT2D_hasZed) {
for (MuonRecHitContainer::const_iterator it2 = muonRecHits_DT2D_hasZed.begin(); it2 != muonRecHits_DT2D_hasZed.end(); ++it2 ) {
// check that global theta of 2-D segment lies within cluster box plus or minus allowed slop
if (((*it2)->globalPosition().theta() < seed_maxY[NNN] + dYclusBoxMax) && ((*it2)->globalPosition().theta() > seed_minY[NNN] - dYclusBoxMax)) {
// check that global phi of 2-D segment (assumed to be center of chamber since no phi hit info)
// matches with cluster box plus or minus allowed slop given that the true phi value could be
// anywhere within a given chamber (+/- 5 degrees ~ 0.09 radians from center)
if(
!(
(
((*it2)->globalPosition().phi() + 0.09) < (seed_minX[NNN] - dXclusBoxMax)
&&
((*it2)->globalPosition().phi() - 0.09) < (seed_minX[NNN] - dXclusBoxMax)
)
||
(
((*it2)->globalPosition().phi() + 0.09) > (seed_maxX[NNN] + dXclusBoxMax)
&&
((*it2)->globalPosition().phi() - 0.09) > (seed_maxX[NNN] + dXclusBoxMax)
)
)
) { // we have checked that the 2Dsegment is within tight theta boundaries and loose phi boundaries of the current cluster -> add it
seeds[NNN].push_back((*it2));
}
}
}
}
// put DT hasphi loop here
if (useDT2D_hasPhi) {
for (MuonRecHitContainer::const_iterator it2 = muonRecHits_DT2D_hasPhi.begin(); it2 != muonRecHits_DT2D_hasPhi.end(); ++it2 ) {
if (((*it2)->globalPosition().phi() < seed_maxX[NNN] + dXclusBoxMax) && ((*it2)->globalPosition().phi() > seed_minX[NNN] - dXclusBoxMax)) {
if(
!(
(
((*it2)->globalPosition().theta() + 0.3) < (seed_minY[NNN] - dYclusBoxMax)
&&
((*it2)->globalPosition().theta() - 0.3) < (seed_minY[NNN] - dYclusBoxMax)
)
||
(
((*it2)->globalPosition().theta() + 0.3) > (seed_maxY[NNN] + dYclusBoxMax)
&&
((*it2)->globalPosition().theta() - 0.3) > (seed_maxY[NNN] + dYclusBoxMax)
)
)
) { // we have checked that the 2Dsegment is within tight phi boundaries and loose theta boundaries of the current cluster -> add it
seeds[NNN].push_back((*it2)); // warning - neeed eta/theta switch here
}
}
}
} // DT2D_hastPhi loop
// put RPC loop here
int secondCh = 0;
DetId detId_prev;
if(seeds[NNN].size()>1){// actually we should check how many chambers with measurements are present
for(unsigned int iRH = 0 ;iRH<seeds[NNN].size() ;++iRH){
if( iRH && detId_prev != seeds[NNN][iRH]->hit()->geographicalId()){
++secondCh;
break;
}
detId_prev = seeds[NNN][iRH]->hit()->geographicalId();
}
}
if (useRPCs && !secondCh && !tooCloseClusters) {
for (MuonRecHitContainer::const_iterator it2 = muonRecHits_RPC.begin(); it2 != muonRecHits_RPC.end(); ++it2 ) {
if (((*it2)->globalPosition().phi() < seed_maxX[NNN] + dXclusBoxMax) && ((*it2)->globalPosition().phi() > seed_minX[NNN] - dXclusBoxMax)) {
if(
!(
(
((*it2)->globalPosition().theta() + 0.3) < (seed_minY[NNN] - dYclusBoxMax)
&&
((*it2)->globalPosition().theta() - 0.3) < (seed_minY[NNN] - dYclusBoxMax)
)
||
(
((*it2)->globalPosition().theta() + 0.3) > (seed_maxY[NNN] + dYclusBoxMax)
&&
((*it2)->globalPosition().theta() - 0.3) > (seed_maxY[NNN] + dYclusBoxMax)
)
)
) { // we have checked that the 2Dsegment is within tight phi boundaries and loose theta boundaries of the current cluster -> add it
seeds[NNN].push_back((*it2)); // warning - neeed eta/theta switch here
}
}
}
} // RPC loop
}
// hand over the final seeds to the output
// would be more elegant if we could do the above step with
// erasing the merged ones, rather than the
for(unsigned int NNN = 0; NNN < seeds.size(); ++NNN) {
if(running_meanX[NNN] == 999999.) continue; //skip seeds that have been marked as used up in merging
//std::cout<<"Next Cluster..."<<std::endl;
segments_clusters.push_back(seeds[NNN]);
}
}
| bool SETPatternRecognition::segmentCleaning | ( | const DetId & | detId, |
| const LocalPoint & | localPosition, | ||
| const LocalError & | localError, | ||
| const LocalVector & | localDirection, | ||
| const LocalError & | localDirectionError, | ||
| const double & | chi2, | ||
| const int & | ndf | ||
| ) |
Definition at line 446 of file SETPatternRecognition.cc.
References BoundSurface::bounds(), Bounds::inside(), minLocalSegmentAngle, outsideChamberErrorScale, GeomDet::surface(), theService, and PV3DBase< T, PVType, FrameType >::z().
Referenced by produce().
{
// drop segments which are "bad"
bool dropTheSegment = true;
const GeomDet* geomDet = theService->trackingGeometry()->idToDet( detId );
// only segments whithin the boundaries of the chamber
bool insideCh = geomDet->surface().bounds().inside(localPosition, localError,outsideChamberErrorScale);
// Don't use segments (nearly) parallel to the chamberi;
// the direction vector is normalized (R=1)
bool parallelSegment = fabs(localDirection.z())>minLocalSegmentAngle? false: true;
if(insideCh && !parallelSegment){
dropTheSegment = false;
}
// use chi2 too? (DT, CSCs, RPCs; 2D, 4D;...)
return dropTheSegment;
}
| void SETPatternRecognition::setServiceProxy | ( | MuonServiceProxy * | service | ) | [inline] |
Definition at line 17 of file SETPatternRecognition.h.
References theService.
Referenced by SETMuonSeedProducer::SETMuonSeedProducer().
{theService = service;}
Definition at line 29 of file SETPatternRecognition.h.
Referenced by produce(), and SETPatternRecognition().
Definition at line 28 of file SETPatternRecognition.h.
Referenced by produce(), and SETPatternRecognition().
int SETPatternRecognition::maxActiveChambers [private] |
Definition at line 25 of file SETPatternRecognition.h.
Referenced by produce(), and SETPatternRecognition().
double SETPatternRecognition::minLocalSegmentAngle [private] |
Definition at line 33 of file SETPatternRecognition.h.
Referenced by segmentCleaning(), and SETPatternRecognition().
double SETPatternRecognition::outsideChamberErrorScale [private] |
Definition at line 32 of file SETPatternRecognition.h.
Referenced by segmentCleaning(), and SETPatternRecognition().
Definition at line 30 of file SETPatternRecognition.h.
Referenced by produce(), and SETPatternRecognition().
Definition at line 36 of file SETPatternRecognition.h.
Referenced by produce(), segmentCleaning(), and setServiceProxy().
bool SETPatternRecognition::useRPCs [private] |
Definition at line 26 of file SETPatternRecognition.h.
Referenced by produce(), and SETPatternRecognition().
1.7.3