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 GetChi2YSlope() const { return fkChi2YSlope; }
+ Double_t GetChi2ZSlope() const { return fkChi2ZSlope; }
+ Double_t GetChi2YCut() const { return fkChi2YCut; }
+ Double_t GetPhiSlope() const { return fkPhiSlope; }
+ Float_t GetNClusters() const;
+ Double_t GetNMeanClusters() const { return fkNMeanClusters; }
+ Double_t GetNSigmaClusters() const { return fkNSigmaClusters; }
Double_t GetFindableClusters() const { return fkFindable; }
Double_t GetMaxTheta() const { return fkMaxTheta; }
Double_t GetMaxPhi() const { return fkMaxPhi; }
Double_t GetPlaneQualityThreshold() const { return fkPlaneQualityThreshold; }
- Double_t GetPIDThreshold(Float_t /*p*/){ return 0.;}
+ Double_t GetPIDThreshold(Float_t /*p*/) const { return 0.;}
Double_t GetRoad0y() const { return fkRoad0y; }
Double_t GetRoad0z() const { return fkRoad0z; }
Double_t GetRoad1y() const { return fkRoad1y; }
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 SetChi2YSlope(Double_t chi2YSlope) {fkChi2YSlope = chi2YSlope;}
+ void SetChi2ZSlope(Double_t chi2ZSlope) {fkChi2ZSlope = chi2ZSlope;}
+ void SetChi2YCut(Double_t chi2Cut) {fkChi2YCut = chi2Cut; }
+ void SetPhiSlope(Double_t phiSlope) {fkPhiSlope = phiSlope;}
+ void SetNMeanClusters(Double_t meanNclusters) {fkNMeanClusters = meanNclusters;}
+ void SetNSigmaClusters(Double_t sigmaNclusters) {fkNSigmaClusters = 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); }
,kVertexConstrained = BIT(17) // Perform vertex constrained fit
,kImproveTracklet = BIT(18) // Improve tracklet in the SA TRD track finder
};
-
+ // Physics reference values for TRD
+ Double_t fkdNchdy; // dNch/dy
Double_t fkMaxTheta; // Maximum theta
- Double_t fkMaxPhi; // Maximum phi
+ Double_t fkMaxPhi; // Maximum phi - momentum cut
Double_t fkRoad0y; // Road for middle cluster
Double_t fkRoad0z; // Road for middle cluster
Double_t fkRoad2z; // Road in z for extrapolated cluster
Double_t fkPlaneQualityThreshold; // Quality threshold
- Double_t fkFindable; // Ratio of clusters from a track in one chamber which are at minimum supposed to be found.
+ Double_t fkFindable; // minimum ratio of clusters per tracklet supposed to be attached.
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 fkChi2YSlope; // Slope of the chi2-distribution in y-direction
+ Double_t fkChi2ZSlope; // Slope of the chi2-distribution in z-direction
+ Double_t fkChi2YCut; // Cut on the Chi2 in y-direction in the likelihood filter
+ Double_t fkPhiSlope; // Slope of the distribution of the deviation between track angle and tracklet angle
+ Double_t fkNMeanClusters; // Mean number of clusters per tracklet
+ Double_t fkNSigmaClusters; // Sigma of the number of clusters per tracklet
+ Double_t fkNClusterNoise; // ratio of noisy clusters to the true one
+ Double_t fkNMeanTracklets; // Mean number of tracklets per track
Double_t fkTrackLikelihood; // Track likelihood for tracklets Rieman fit
Double_t fSysCovMatrix[5]; // Systematic uncertainty from calibration and alignment for each tracklet
Int_t fNumberOfPresamples; // number of presamples
Int_t fNumberOfPostsamples; // number of postsamples
- ClassDef(AliTRDrecoParam, 4) // Reconstruction parameters for TRD detector
+ ClassDef(AliTRDrecoParam, 7) // Reconstruction parameters for TRD detector
};