ECAL Interaction layer PFA Template Track/CalCluster Association Track extrapolation Mip finding Shower interaction point Shower cluster pointing Shower.

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Presentation transcript:

ECAL Interaction layer PFA Template Track/CalCluster Association Track extrapolation Mip finding Shower interaction point Shower cluster pointing Shower proximity match ECALHCAL Track Mips Interaction point Shower Cluster Pointing Photon ID Shower interaction layer Longitudinal E profile (H-Matrix) Interaction layer Cluster Merge ECAL HCAL Neutral Hadron ID Shower interaction layer EM/HAD Cluster Merge

Track-Mip/Shower Interaction Point Algorithm -> exponential behavior for each section of CAL – 20/10 layer ECAL sections and 34 layer HCAL Also some non-interacting pions Features : - CalorimeterHits matched layer-by-layer to extrapolated tracks - Uses hit position and calorimeter layer number from detector geometry - No hit energy used, mip test based on internally-calculated hit densities - Interaction point set at layer where hit density too large or 0 in extrapolated region - Mipclusters, interaction spacepoint linked to tracks With Test Beam data now : Algorithm tests Optimize density parameter in algorithm Test interaction spacepoint distribution MC shower model tests Particle propagation –> interaction layer Detector tests Number of hits per mip – delta rays Layer number rms90 = 4.5 cm Mip endpoint – MC Track endpoint Difference (mm) Interaction Layer all Tracks

Features : - Uses IL spacepoint from Track-Mip Alg. - Cluster CalorimeterHits with DT clusterer – 4 hit min for principal axes determination - Needs full CalorimeterHit info and detector geometry (including sampling fractions) - Compare cluster pointing to IL spacepoint direction and IP direction : If IL spacepoint comparison points at IL spacepoint, linked to track Else if IP direction comparison small enough -> points at IP Else NP (non-pointing) Cluster With Test Beam data now : Algorithm tests Cluster algorithms (DT + others) MC shower model tests Cluster properties (# frags, size, shape) Cluster fragment pointing properties Detector tests ? (haven’t thought of any yet) Cluster Pointing Algorithm 1.1 Clusters/pion DT Clustering with 4 hit minimum, after mip finder 1-10 GeV pions, degrees 2.4 Clusters/pion0.4 Clusters/pion IL SP PointingIP PointingNon-Pointing Number IP “Photon-like”IP “Charged Particle-like” 25% “photons”75% “charged particles”

Testing DAQ -> Analysis Chain with Particle Flow Algorithms LCIO Output - CalorimeterHits, TrackHits, etc Track-Mip/Shower Interaction point Algorithm Cluster Pointing Algorithm Shower Proximity Algorithm Track Extrapolation Simulation DigiSim – hit digitization with noise, timing cuts, threshold cuts, etc. Data DAQ Processors Testable Algorithms - Software package – org.lcsim JAVA source code - Geometry – compact.xml -> GeomConverter -> detector.lcdd - Conditions data (geometry files, sampling fractions, others) kept in detector zip file accessed by analysis, simulation Parameter tuning with test beam (limited MC tests) Unnecessary unless test beam setup includes tracker Algorithms independent of “data” source