1. Sampling Calorimeter Reconstruction Issues and Approaches: An Overview
Gary Bower & Ron Cassell, SLAC
May 22, 2003
SLAC LCD Workshop

2. (Evolutionary) Approaches in Order of Sophistication
Naïve energy flow approach
Crude cluster finding approach (old)
Cluster identification (current)
Refined cluster identification (future)
Bower/Cassell
SLAC LCD Workshop- 5/22/03

3. Why Energy Flow?
Bower/Cassell
SLAC LCD Workshop- 5/22/03

4. Naïve Energy Flow Three clear jets of particles in event.
Individual tracks identify charged particles.
No individual clusters to identify neutral particles.
Thus, try to use the entire swarm of hits.
Extend tracks through cal, eliminate charged hits.
Left with “energy flow” of neutral hits.
Necessary for coarse grained cals but not for fine grained.
Bower/Cassell
SLAC LCD Workshop- 5/22/03

5. Pictures can be misleading
Most hits are very low energy photons.
3D structure projected on to 2D.
It is possible to identify clusters of hits from individual particles in the cal.
Bower/Cassell
SLAC LCD Workshop- 5/22/03

6. Primer on cal clusters Principles Three “signature” types:
Interaction length (hadronic) is long.
Radiation length (EM) is short.
Golf ball meets bowling ball.
Three “signature” types:
minimum ionization (mu, charged hadron)
electromagnetic showers (electron, gamma)
hadronic showers (charged & neutral hadron)
Bower/Cassell
SLAC LCD Workshop- 5/22/03

7. Gamma shower cartoon
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SLAC LCD Workshop- 5/22/03

8. Charged hadron shower cartoon
Bower/Cassell
SLAC LCD Workshop- 5/22/03

9. Neutral hadron shower cartoon
Bower/Cassell
SLAC LCD Workshop- 5/22/03

10. Crude cluster finding approach
Make contiguous hits clusters (not regional)
Make cluster energy cut.
Assume EM clusters are gammas.
Assume Had clusters have pion mass.
Assume all point directly back at IP.
Treat each cluster as a particle and analyze the event using jet finders, etc.
Bower/Cassell
SLAC LCD Workshop- 5/22/03

11. Crude clusters results
Using neural net can identify event type as qqbar, ZZ, WW, ttbar, ZH(120).
Bower/Cassell
SLAC LCD Workshop- 5/22/03

12. % Efficiency with NN 91 76 90 66 97 53 96 74 100 86 Gen Level
Sim Level qq 91 76 WW 90 66 ZZ 97 53 ZH 96 74 tt
100 86
Bower/Cassell
SLAC LCD Workshop- 5/22/03

13. % Correct ID with NN 95 74 98 60 87 97 64 86 Gen Level Sim Level qq WWZZ 87 ZH 97 64 tt 86
Note: assumes equal cross section for all 5 processes.
Bower/Cassell
SLAC LCD Workshop- 5/22/03

14. ZH event id rate – sim level
% identified as ZH
were actually event type: 6% qq 8% WW
15% ZZ
67%* ZH 5% tt
Note: assumes equal cross section for all 5 processes.
Bower/Cassell
SLAC LCD Workshop- 5/22/03

15. Cluster identification
Use cluster properties + a neural net to identify the particle that created the cluster.
The ClusterID algorithm ( by Bower, Cassell, Pathek) currently identifies gammas, charged hadrons, neutral hadrons and fragments. Available in CVS.
Web page is underdevelopment.
Bower/Cassell
SLAC LCD Workshop- 5/22/03

16. ClusterID recon efficiency
SD detector with no gap between EM and Had cals.
Bower/Cassell
SLAC LCD Workshop- 5/22/03

17. Zmass at Zpole
Bower/Cassell
SLAC LCD Workshop- 5/22/03

18. Refined Cluster Identification
What limits efficiencies and purities of ClusterID?
Using tools like Cluster Analysis (Ron’s talk) can identify which cases are IDed wrong.
Then think up new methods for those cases.
Eg, fragments
Point fragments back to their origin.
Eg, overlapping clusters
Raise hit E level to separate clusters.
Shape algorithm to see two gamma bulges.
Bower/Cassell
SLAC LCD Workshop- 5/22/03

19. The essential elements
Calorimeter hits (digital or analog).
A cluster builder technique
A cluster identification technique (can use tracking information).
To reconstruct events:
Make a reconstructed particle for each cluster and track.
To study efficiency (& purity):
Make a clusterlist (& reconstructed particles).
Bower/Cassell
SLAC LCD Workshop- 5/22/03

20. LCD Recon is Flexible
Can use more than one algorithm to do the same task in an event loop (eg, tracking or cal recon).
Thus, event by event in one job, can simultaneously compare several algorithms to accomplish a task.
See how each performs on the same data.
Combine results of several algorithms.
Bower/Cassell
SLAC LCD Workshop- 5/22/03

21. Neural Nets and collaboration
Can view different on-going studies as trying different techniques to make and identify clusters. (eg, ClusterID uses contiguous hits cluster builder with ~10 measures on each cluster, others use cone algorithm to make clusters with their measures, etc)
Can combine methods using a neural net.
Bower/Cassell
SLAC LCD Workshop- 5/22/03

22. An opinion on terminology
When you are making clusters and identifying their origin, you are no longer doing energy flow. You are doing traditional particle reconstruction in a calorimeter. We should stop calling these techniques “energy flow” and start calling them “calorimeter particle reconstruction”.
Jean-Claude and Henri have expressed the same opinion.
Bower/Cassell
SLAC LCD Workshop- 5/22/03