1. Charge Subtraction, Weizmann Clusterizer, and Pattern Recognition Mihael Makek Weizmann Institute of Science HBD Fest, Stony Brook, 2010

2. HBD occupancy in the most central events > 95 % Subtracted is the average charge per cell:  on event-by-event basis  for each particular HBD module  taking into account pad area Occupancy reduced to ~30 % in the most central events Charge Subtraction before subtraction: after subtraction:

3. The subtraction is justified by the fact that the number of the scintillation photons grows linearly with number of tracks in CA The example shows this for a single HBD module: HBD – charge subtraction The bars represent the sigma of the gaussian fit to the average charge distribution (for given centrality)

4. Seed preblobs on pads with q > 3 pe Add the six neighbors if they are above threshold (centrality dependant ~ 0-2 pe) Merge preblobs if they have overlapping pads with the „main“ preblob Match the cluster to the closest hit Weizmann Clusterizer

5. The charge measured in HBD is obtained after matching cuts on hbddphi and hbddz The background is estimated by swapping (CA tracks projected to a different module, x is swapped) The background is normalized to the matching distributions tail- to-tail and subtracted Weizmann Clusterizer signal+background background

6. The random matching in HBD is due to the fact that CA electron tracks originating from conversions in and after the HBD backplane have no real matching hit in the HBD The rejection of the random is achieved in three steps: 1.Requirying positional matching of the CA track projection and HBD cluster (3 sigma) 2.Rejection of 1 pad clusters 3.Rejection of clusters with the maximum pad charge below a certain threshold. This threshold is centrality dependent The preblobs with q max > 80 pe are rejected Pattern recognition is done on preblob level Pattern Recognition signal background peripheral central

7. Pattern Recognition Before p.r.After p.r. S/R ~ 1.1 S/R ~ 2.2 The pattern recognition significantly improves HBD signal to randoms, but costs the efficiency The efficiency is estimated from MC simulation of single electrons embedded with HBD MB data Steps applied Case (MB events)  Pure MC93 % 1 + 2MC embedded with HBD74 % 1 + 2 + 3MC embedded with HBD + pattern recognition 67 %

8. variables monitored, but not as useful: q max -q min q min /q max q min /q tot q max /q tot variables defined but not yet used: (y loc,z loc ) of all pads in the cluster Pattern Recognition

9. Centrality No of. el HBD/CA Signal /Random Efficiency (embedding) peripheral0.238.40.93 central0.430.80.67 Summary Centrality No of. el HBD/CA Signal /Random Efficiency (embedding) peripheral0.2267.30.92 central0.281.60.55 I. WIS Clusterizer with clustersize>1 && q max <80 II. Pattern recognition + I.