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Calorimeter reconstruction

Calorimeter reconstruction. V. Breton Calorimeter software meeting August 2001. CaloParticle class (I. Belyaev). Hypothesis on the particleID Likelihood for given hypothesis References to ECAL/HCAL clusters References to SPD/PRS digits Z-position of reference plane Energy

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Calorimeter reconstruction

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  1. Calorimeter reconstruction V. Breton Calorimeter software meeting August 2001

  2. CaloParticle class (I. Belyaev) Hypothesis on the particleID • Likelihood for given hypothesis • References to ECAL/HCAL clusters • References to SPD/PRS digits • Z-position of reference plane • Energy • X-position at reference frame • Y-position at reference frame • 3x3 covariance matrix

  3. Building CaloParticles (1) • Take the list of clusters delivered by ECAL clustering algorithm • Two strategies • Create one CaloParticle per cluster • For each type of CaloParticle (photon, electron, positron,pi0,…) , loop on all the clusters

  4. Option 1 : start with one CaloParticle per CaloCluster • Loop on the CaloParticles • Look for SPD/PRS hits matching ECAL cluster • No hypothesis on particle charge • Look for HCAL hits matching ECAL cluster • Compute estimator for all possible 1-cluster particle ID’s • Electromagnetic vs Hadronic ? (SPD/PRS/HCAL) • Merged Pi0 vs photon/e+/e- ? (ECAL) • Highest score becomes the CaloParticle ID hypothesis • Output : list of 1-cluster CaloParticles with a particle ID hypothesis and a score for this hypothesis

  5. Option 1, step 2 : look for « virtual » CaloParticles • « virtual » CaloParticles are associated to more than one ECAL CaloCluster • Bremsstrahlung : loop on position-aligned CaloClusters • Overlapped pi0 : loop on neighbouring CaloClusters • Resolved pi0 : loop on all CaloClusters • Output : list of new CaloParticles associated to 2 clusters • A CaloCluster can be pointed at by 2 CaloParticles • Example : a photon cluster is pointed at by a CaloParticle photon and a « virtual » CaloParticle pi0

  6. Option 2 : look for CaloParticles • Start with a given ID hypothesis (electron for instance) • Loop on all the CaloClusters • Look for associated SPD/PRS hits knowing ID hypothesis • Look for associated HCAL cluster • Compute estimator • Above a given threshold, create the CaloParticle object

  7. Option 2, step 2 : look for CaloParticles with 2 CaloClusters • Bremsstrahlung • Compute the list of CaloParticles identified as electrons (see previous slide) • For each electron CaloParticles, look for CaloClusters aligned vertically • Compute bremsstrahlung recovery • If successful, create new CaloParticle • 2 CaloParticles (bremsstrahlung & electron) pointing to the same CaloCluster

  8. Option 2 : pi0 • Merged pi0 • Compute the list of CaloParticles identified as photons (only or electromagnetic ?) • Look for neighbouring CaloClusters & compute merged pi0 estimator • Resolved pi0 • Compute the list of CaloParticles identified as photons (only or electromagnetic ?) • Compute resolved pi0 estimator for all pairs of 2 CaloParticles

  9. Advantages of Option 2 • PRS/SPD hits are searched with a charge hypothesis • Easy answer to questions « give me electrons above a given energy »

  10. Questions related to option 2 • For each possible ID hypothesis, loop on all CaloClusters or only on the CaloClusters not yet associated to a CaloParticle ? • Example : look first for electrons, then for photons • A CaloCluster can have a large score for several ID’s • Which to choose ? Highest score ? • How to fix the threshold on the score if one loops only on one ID hypothesis ? • How to know it looks better like a pion than like a photon if you don’t check it ?

  11. Scoring a 1-Cluster CaloParticle ID hypothesis • Score is probability to have a given ID knowing • CaloCluster (number of cells, cells energy, neighbours, cluster position, shape) • Associated SPD/PRS hits • Associated HCAL hits • Scoring algorithm should be able to learn from simulation and clean data samples • Neural Network • Hidden Markov Models • …

  12. Option 3 • For each CaloCluster, loop on all ID hypothesis • Look for SPD/PRS hits knowing ID hypothesis • Look for HCAL hits • Compute score • Highest score is the particle ID

  13. Hidden Markov Model • From a set of continuous variables of a given event, the model is able to choose the most probable category to which this event belongs. • To do this, the model is trained on events known to belong already to given categories

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