Double_t GetChi2Y() const { return fkChi2Y; }
Double_t GetChi2Z() const { return fkChi2Z; }
+ Double_t GetChi2YCut() const { return fkChi2YCut; }
+ Double_t GetChi2ZCut() const { return fkChi2ZCut; }
+ Double_t GetPhiCut() const { return fkPhiCut; }
+ Double_t GetMeanNclusters() const { return fkMeanNclusters; }
+ Double_t GetSigmaNclusters() const { return fkSigmaNclusters; }
Double_t GetFindableClusters() const { return fkFindable; }
Double_t GetMaxTheta() const { return fkMaxTheta; }
Double_t GetMaxPhi() const { return fkMaxPhi; }
Bool_t IsVertexConstrained() const { return TestBit(kVertexConstrained); }
Bool_t HasImproveTracklets() const { return TestBit(kImproveTracklet); }
-
+ void SetMaxTheta(Double_t maxTheta) {fkMaxTheta = maxTheta;}
+ void SetMaxPhi(Double_t maxPhi) {fkMaxPhi = maxPhi;}
void SetFindableClusters(Double_t r) {fkFindable = r;}
void SetChi2Y(Double_t chi2) {fkChi2Y = chi2;}
void SetChi2Z(Double_t chi2) {fkChi2Z = chi2;}
+ void SetChi2YCut(Double_t chi2YCut) {fkChi2YCut = chi2YCut;}
+ void SetChi2ZCut(Double_t chi2ZCut) {fkChi2ZCut = chi2ZCut;}
+ void SetPhiCut(Double_t phiCut) {fkPhiCut = phiCut;}
+ void SetMeanNclusters(Double_t meanNclusters) {fkMeanNclusters = meanNclusters;}
+ void SetSigmaNclusters(Double_t sigmaNclusters) {fkSigmaNclusters = sigmaNclusters;}
void SetClusterSharing(Bool_t share = kTRUE) { SetBit(kClusterSharing, share);}
void SetImproveTracklets(Bool_t improve = kTRUE) { SetBit(kImproveTracklet, improve);}
void SetVertexConstrained(Bool_t vc = kTRUE) { SetBit(kVertexConstrained, vc); }
Double_t fkFindable; // Ratio of clusters from a track in one chamber which are at minimum supposed to be found.
Double_t fkChi2Z; // Max chi2 on the z direction for seeding clusters fit
Double_t fkChi2Y; // Max chi2 on the y direction for seeding clusters Rieman fit
+ Double_t fkChi2YCut; // Cut on the Chi2 in y-direction in the likelihood filter
+ Double_t fkChi2ZCut; // Cut on the Chi2 in z-direction in the likelihood filter
+ Double_t fkPhiCut; // Cut on the deviation of the phi angles between tracklet and track fit (lik. filter)
+ Double_t fkMeanNclusters; // Mean of the distribution of the number of clusters per tracklet
+ Double_t fkSigmaNclusters; // Sigma of the distribution of the number of clusters per tracklet
Double_t fkTrackLikelihood; // Track likelihood for tracklets Rieman fit
Double_t fSysCovMatrix[5]; // Systematic uncertainty from calibration and alignment for each tracklet