1. Testing the T-tracker Straw Clustering Code and Stereo Hit Finding Clustering Code Recently implemented by Hans Wenzel (MakeStrawCluster_pluggin.cc) StrawClusters are now a MU2E class Stereo Hits are NOT a MU2E class. Code currently in development (see ReadStrawCluster_pluggin.cc). Why is this important ? Our approach: Stereo Hits are starting seeds to send to a helical trajectory fitter. We need to know the efficiency for finding stereo hits, among other things. Stereo Hits are formed by clusters (more later). In the future, we will add (correlated or random) noise straw hits to access the rate of false stereo hits. Hans Wenzel, Hogan Nguyen, Jan 19 th, 2011

2. Straw Clustering Code Implemented by Hans Wenzel. Clustering finds clumps of neighboring straws (above threshold) within a Panel

3. Signal Electrons Generated Cos (Polar Angle) = 0.5 NO NOISE HITS The number of isolated straw hits (n=1) = 25% Number of Straws (above threshold) per Cluster

4. 3 mm distance between wires 0.5 mm For normal trajectories, rate of singlets = 1 mm/ 3 mm = 33% We find 25% rate of singlets (independent of hit threshold). Trajectory generated is 30 degrees from normal. Higher efficiency probably due to non-normal track angles.

5. Currently, finding Stereo Hits ONLY WITHIN ONE Device Later should generalize Stereo Hit finding to within ONE STATION (= 2 devices) DeviceStation = 2 devices back-to-back

6. Number of Clusters Found Per Device (0-35) at least 2 are required to form a “Stereo Hit” Rate is too low

7. Number of Stereo Hits Found Per Device (0-35) Rate is too low

8. Number of STEREO Hits Found Per Event at least 3 needed to fit to a circle (48% efficiency)

9. Clustering and Stereo Hit finding seems to work This strategy yields too low efficiency for finding Stereo Hits. But it is a good start. Stereo hit finding should extend to a station (rather than a device) Can access later if stereo hit finding should include three-cluster intersections (currently only 2 are used)