1 Calorimetry Simulations Norman A. Graf for the SLAC Group January 10, 2003

2 Analysis Infrastructure  Redefine detector segmentation: Detector definitions changed and CalorimeterHits rewritten for each event. Allows using existing data sets for comparing different detector segmentations. Generate events with fine segmentation, gang at analysis level to study effects of cell size.  Redefine particles (MCParticle) to which calorimeter energy is assigned. Especially useful for particles that interact in inner walls of calorimeters. Finer control over which MC particle is considered shower initiator.

3 Clustering Algorithms  Current clusterer (SimpleClusterFinder) : All adjacent hits in a calorimeter form a cluster Extend idea of adjacency across EM-HAD border. Adjacency extended to an integer number of bins in theta, phi, and layer. Need to extend across Barrel-Endcap borders.  Fixed-Cone clusterer developed for analysis of EM showers. Fast, efficient.

4 Cluster Algorithm Evaluation  Clustering analyzer in progress: will ultimately produce efficiency plots, purity plots, and energy resolution for each class of particle. (EM, Charged hadron, Neutral hadron)

5 Energy Assignments  Work continuing to understand sampling fraction differences. EM vs HAD Barrel vs Endcap (esp. in 5T SD)  Gismo/Geant4 differences in energy deposition under investigation: Total fraction of photon energy to ionization, fraction of ionization in active material, and effect of magnetic field all different in Geant4.

6 ClusterID Algorithm  Goal is to identify particle type (photon, charged hadron, neutral hadron, …, fragment) that created each cal cluster.  Based on a set of discriminators measured for each cluster ( shape and pointing parameters, …)  Now using a Neural Net for discrimination.

7 ClusterID Neural Net  Input variables for the neural net are composed of cluster shape quantities, e.g. Normalized cluster energy tensor eigenvalues Cluster extent, number of hit cells in cluster, cluster energy,… Position and angular difference wrt IP  Total of 15 inputs  4 outputs: Photon, Charged Hadron, Neutral Hadron, Fragment (assign cluster highest ID).

8 Neural Net Training  Neural Nets have been trained on single particle samples.  Tested on both single particle samples and physics samples.  Work ongoing to refine input variables.  Framework and trained nets exist and have been released in latest hep.lcd distribution.  Aim to release fully retrainable cluster ID and general NN application code when JAS3+LCD is ready.

9 ClusterID Current Status  Study Z decays at Z pole in SD.  Gives Z mass width ~twice the width of perfect reconstruction.  Correctly IDs 90% of gamma energy.  Incorrectly IDs 6% of gamma energy.  Correctly IDs 66% of neutral had energy.  Incorrectly IDs 27% of neutral had energy. (27% goes to 42% misID with cal gap)

10 Zmass at Zpole

11 EM Id  Developed simple fixed-cone algorithm for finding EM clusters. Fast, efficient.  Implemented fully covariant  2 calculation for longitudinal shower shape analysis.  Use shower width for  -  0 discrimination.  In addition, use track-match and E/p for electron id.

12 Cone Algorithm  Currently using fixed cone radius of 0.03 in ,  space on EM Calorimeter hit cells. Based on energy contained within cone. Based on number of clusters.  Could also use a cone radius based on energy of seed cell.  Currently split clusters whose cones overlap by associating cells to nearest cone axis.  Could also search for NN clusters within cone.

13 Longitudinal HMatrix  Use longitudinal energy depositions and their correlations to create a cluster  2.  Mild, smooth energy dependence (~logE)

14 Charged Hadron Id (No clustering)  Continuing to characterize pion shower shapes in calorimeters as function of momentum and direction.  PionShower class being developed to encapsulate the association of hit calorimeter cells with extrapolated tracks. Follows MIP trace to shower start. Characterize hit-track association with  2. Will allow association to proceed until a limit is reached on either match  2 or E/p.

15 ReconstructedParticle  A class which encapsulates the behavior of an object which can be used for physics analysis. mirrors MCParticle  Kinematics determined by track momentum or calorimeter cluster energy at time of creation.  ID determined later by particle ID algorithms, e.g. track dE/dx, cluster shape, or combination of detector element variables.

16 Detector Designs  New SD geometry without physical gap between EM and HAD. Propose strongback followed by sampling layer.  Working with T. Behnke, have first implementation of “T” detector. Approximation to Tesla detector using simplified geometries (barrels and disks) and projective readout. Should simplify EFlow analysis comparisons.

17 ~Fast Simulations  Current fast simulation does not populate calorimeter cells, only smears “Clusters”.  Working on more realistic fast simulation:  Using parameterizations for longitudinal and lateral shower shapes. Fast prototyping of materials, segmentation, etc.  Using a shower library. Fast simulation of large samples for fixed detector.

18 New Functionality  Ganging of calorimeter cells during analysis.  Simple (NN) clustering across EM-HAD.  User-defined neighborhood size for clustering.  Cone algorithm + HMatrix for EM showers.  Neural Net applied to ClusterID.  ReconstructedParticle definitions arising.  Integrated Eflow package being developed.  T Detector implemented for comparison.

19 Acknowledgements  Have just presented an overview of work being done.  Details can be found in talks presented (or to be presented) in ALCPG calorimeter meetings.  Thanks to: T. Behnke, G. Bower, R. Cassell, T. Johnson, W. Langeveld